1564 Data Scientist Jobs - Page 50

Role Overview: We are looking for an experienced Data Scientist specializing in Market Mix Modeling (MMM) to join our team. The candidate will be responsible for developing predictive models to measure marketing effectiveness, optimize budget allocation, and derive actionable insights from large datasets. This role requires expertise in statistical modeling, machine learning, and data visualization to support data-driven decision-making for our premium FMCG Client. Roles & Responsibilities: 1.Develop and implement Market Mix Models (MMM) to evaluate the impact of marketing activities on sales and brand performance. 2.Analyze large-scale marketing, sales, and consumer data from multiple sources to uncover trends and insights. 3.Apply statistical techniques (e.g., regression modeling, time-series analysis, Bayesian modeling) to measure ROI across marketing channels. 4.Work with cross-functional teams to optimize media spend and marketing investments. 5.Use Python, R, SQL, and SAS for data extraction, cleaning, and modeling. 6.Build automated reporting dashboards using Power BI, Tableau, or Excel to visualize model outputs. 7.Conduct scenario analysis and simulations to forecast business outcomes. 8.Communicate technical findings to non-technical stakeholders, ensuring data-driven decision-making. 9.Continuously improve models by incorporating machine learning algorithms and external factors (macroeconomic, seasonal, competitive). 10.Collaborate with business teams to translate insights into actionable marketing strategies. Desired Candidate Skills: Bachelors/Masters degree in Statistics, Data Science, Economics, Mathematics, or a related field. 4-7 years of hands-on experience in Market Mix Modeling (MMM), econometrics, or marketing analytics. Strong expertise in statistical analysis, regression modeling, and causal inference techniques. Proficiency in Python, R, SQL, SAS, and experience with ML libraries (Scikit-learn, TensorFlow, XGBoost). Hands-on experience with BI tools (Power BI, Tableau, Looker) for data visualization. Prior experience in FMCG, Retail, or Consumer Goods industry is highly preferred. Strong business acumen and ability to translate data into actionable insights. Excellent stakeholder management and communication skills. Join us in leveraging data-driven strategies to optimize marketing effectiveness and drive business growth.

Key Responsibilities: Design, develop, and optimize NLP models for text classification, sentiment analysis, chatbots, and language generation. Work with deep learning models such as Transformers (BERT, GPT, LSTMs) to enhance NLP capabilities. Implement and fine-tune machine learning algorithms for text analytics and information extraction. Collaborate with data engineers and software developers to integrate NLP solutions into real-world applications.

Key Responsibilities: Design, develop, and optimize NLP models for text classification, sentiment analysis, chatbots, and language generation. Work with deep learning models such as Transformers (BERT, GPT, LSTMs) to enhance NLP capabilities. Implement and fine-tune machine learning algorithms for text analytics and information extraction. Collaborate with data engineers and software developers to integrate NLP solutions into real-world applications.

Key Responsibilities: Design, develop, and optimize NLP models for text classification, sentiment analysis, chatbots, and language generation. Work with deep learning models such as Transformers (BERT, GPT, LSTMs) to enhance NLP capabilities. Implement and fine-tune machine learning algorithms for text analytics and information extraction. Collaborate with data engineers and software developers to integrate NLP solutions into real-world applications.

Key Responsibilities: Design, develop, and optimize NLP models for text classification, sentiment analysis, chatbots, and language generation. Work with deep learning models such as Transformers (BERT, GPT, LSTMs) to enhance NLP capabilities. Implement and fine-tune machine learning algorithms for text analytics and information extraction. Collaborate with data engineers and software developers to integrate NLP solutions into real-world applications.

Key Responsibilities: Design, develop, and optimize NLP models for text classification, sentiment analysis, chatbots, and language generation. Work with deep learning models such as Transformers (BERT, GPT, LSTMs) to enhance NLP capabilities. Implement and fine-tune machine learning algorithms for text analytics and information extraction. Collaborate with data engineers and software developers to integrate NLP solutions into real-world applications.

Job Summary: The ideal candidate will have a strong background in data analysis, machine learning, and statistical modeling, along with the ability to derive actionable insights from complex data sets. Responsibilities: Data Collection and Preprocessing: Acquire, clean, and preprocess data from various sources to ensure data quality and reliability. Work with big data technologies and tools for efficient data processing. Exploratory Data Analysis (EDA): Conduct exploratory data analysis to understand data patterns, trends, and anomalies. Identify relevant variables and features for further analysis. Statistical Modeling and Machine Learning: Develop and implement statistical models and machine learning algorithms to solve business problems. Perform predictive modeling, classification, clustering, and regression analysis as needed. Evaluate model performance and make improvements using techniques like cross-validation and hyperparameter tuning. Collaboration and Communication: Collaborate with cross-functional teams, including data engineers, business analysts, and software developers. Communicate technical concepts and results to non-technical stakeholders in a clear and understandable manner. Provide guidance and support to team members on data-related tasks and projects. Qualifications: Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or a related field. Proven experience in data analysis, machine learning, and statistical modeling. Proficiency in programming languages such as Python, R, or SQL. Familiarity with GenAI and implementing LLM Familiarity with machine learning libraries/frameworks (e.g., TensorFlow, scikit-learn, PyTorch). Experience with big data platforms (e.g., Hadoop, Spark) and cloud services (e.g., AWS, Azure) is a plus. Strong analytical and problem-solving skills with attention to detail. Excellent communication and collaboration abilities. Ability to work independently and manage multiple projects simultaneously. Note: Job Location: Chennai, Bangalore, Pune, Coimbatore, Mumbai Work Mode: Hybrid ( 3 Days WFO and 2 Days WFH) Work Time: 2pm to 11pm ( UK Shift time) Notice Preferred: 0-30 Days

Job Title: Data Scientist Location: PAN India (Infosys DCs) Job Type: Full-time Responsibilities: Analyze large datasets to extract actionable insights and perform EDA Develop, tune and implement machine learning models and algorithms – supervised and unsupervised. Interpret and explain the model performance to business team. Collaborate with cross-functional teams to understand business requirements and translate them into data solutions. Design and conduct experiments to validate hypotheses and improve models. Communicate findings and recommendations to stakeholders through reports and presentations. Stay updated with the latest industry trends and technologies in data science. Requirements: Bachelor's or Master's degree in Data Science, Statistics, Computer Science, or a related field. Minimum of 3 years of experience in data science or a related role. Proficiency in programming languages such as Python, R, or SQL. Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) Knowledge of cloud platforms (e.g., AWS, Azure). Understanding of statistical methods and data mining techniques. Knowledge / experience of GenAI will be a plus. Minimum 3 years of experience as a Data Analyst or a Data Scientist Show more Show less

raja.a@honeybeetechsolutions.com resume share to • 4 to 10 years of hands-on experience in Data Science, Machine Learning, or Predictive Analytics. • Strong programming skills in Python and proficiency in deep learning frameworks and libraries such as TensorFlow, PyTorch, Pandas, and Scikit-Learn. • Strong expertise in Transformer Models, LSTM, and Time-Series Analysis. • Experience with Unsupervised Learning Techniques (DBSCAN, UMAP, KMeans).

Department/ Group : Advanced Rail Technology Job Description, duties & responsibilities Progress Rails data science team is looking for a motivated and talented Data Scientist who will primarily focus on developing Machine Learning/Artificial Intelligence based data models for condition-based monitoring of its assets. In this role, the candidate will contribute to the design, development and deployment of world class rail products and services vital to our customers needs. Reporting to the Director of Data Science, this role will enable innovative, strategic, and high-tech solutions for the rail industry through the application of specialized knowledge, skills, and abilities. Work involves independent judgement, problem solving skills, resourcefulness, teamwork, and creativity in ambiguous situations. A high degree of personal initiative is a prerequisite. Typical data science team efforts are a combination of some, or all the key job elements listed below. The ideal candidate is an experienced self-starter, strong attention to details, with excellent written and verbal skills. Enjoys working in a collaborative, fast-paced, environment and is willing to take on roles outside of comfort zone. Technical aptitude and being well versed in Machine Learning and Data Science tools and processes is a must. The role will work closely with the different engineering teams. Key Job Elements Contribute to the design, development, testing, and deployment of software systems and applications. Processing, cleansing, and verifying the integrity of data used for analysis. Apply Machine Learning and other advanced analytical techniques to develop models for condition-based monitoring of locomotive systems. Apply Natural Language Processing (NLP) and Large Language Model (LLM) to support text mining, document summarization and others. Understand the business needs and develop data-based solutions. Doing ad-hoc analysis and presenting results in a clear manner Supporting field reported issue resolution through data analysis System integration of Machine Learning models Mentor and assist data scientists providing technical assistance and direction as needed Technical Skill Experienced Data Scientist with 7+ years experience in Data Extraction, Data Modelling, Data Wrangling, Statistical Modeling, Data Mining, Machine Learning and Data Visualization. Expertise in transforming business resources and requirements into manageable data formats and analytical models, designing algorithms, building models, developing data mining, and reporting solutions that scale across a massive volume of structured and unstructured data. Proficiency in managing entire data science project life cycle and actively involved in all the phases of project life cycle including data acquisition, data cleaning, data engineering, features scaling, features engineering, testing and validation and data visualization. Expertise in applying Machine Learning algorithms (such as Regression Models, XGBoost, Neural Network, and others) for predictive analytics. Experience in Generative AI developing LLM based solutions for document search/summarization using RAG architecture. Expertise in applied statistics skills, such as distributions, statistical testing, regression, etc. Strong experience with Python, SQL, and R. Experience and knowledge of AWS cloud which includes Machine Learning related services, S3, Elastic search, Lambda, and others. Experience in integrating Machine Learning models into larger deployed systems. Proficiency in data visualization tools such as PowerBI, Python Matplotlib, R Shiny to create visually powerful and actionable interactive reports and dashboards. Experience in Natural Language Processing and Text Mining. Strong business sense and abilities to communicate data insights to both technical and non-technical clients. Competent to perform all job duties without close supervision. Desired : Rail industry experience Experience in developing models using telematics (sensor) data from equipment such as engines, machines, and others. Qualifications and Education Requirements B.S, M.S, or PhD degree in quantitative discipline such as data science, data analytics, computer science, engineering, statistics, mathematics, or other related degree. 7+ years of data science experience with B.S., or 5+ years of experience with Advanced degrees 7+ years of experience with Python, R, SQL, and relational data bases

Must understand Gen AI models o Hands on with Python with basic ML libraries o Building NLP engine (pre-processing, modelling) o Understanding of Deep Learning frameworks for NLU, NLG & MLOps o Docker containerization, building CI/CD pipeline o Hands on with GCP Data Science tool for Building, Training, Deploying Models, especially Vertex Role & responsibilities Preferred candidate profile

Company Description Unlox Academy is an innovative education platform that focuses on providing a seamless learning experience for students. The academy aims to make education accessible, engaging, and life-changing by delivering world-class education from genuine experts through AI-based learning. Unlox Academy is committed to helping students unlock their true potential and discover new possibilities in their educational journey. Role Description This is an internship role for a Data Scientist at Unlox Academy, located in Bengaluru. The Data Scientist will be responsible for tasks such as data analysis, data visualization, and data analytics on-site. The role will involve working with statistical models and interpreting data to derive insights for the academy's educational initiatives. Qualifications Data Science and Statistics skills Data Analytics and Data Analysis skills Data Visualization skills Experience with statistical models and interpreting data Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or related field Stipend- 20000 for 4 months during internship post which employment would be offered from 4-6 lpa Show more Show less

About the Role Mergen Partners is seeking a Data Scientist to join a cutting-edge AI and Data Science team. In this role, you’ll design predictive models and data-driven solutions that directly influence critical business areas such as pricing strategy, customer behavior analysis, and resource optimization. If you thrive on solving complex problems with AI and want to see your work drive tangible outcomes, this is your opportunity to make an impact. What You’ll Do Lead Analytical Strategy: Drive innovation by proposing new statistical frameworks, testing methodologies, and infrastructure enhancements to unlock business value. Collaborate Cross-Functionally: Partner with stakeholders to define challenges, develop hypotheses, and transform raw data (including conversational data) into actionable insights and scalable models. Own End-to-End Analytics: Conduct descriptive/prescriptive analyses, design experiments (A/B, multi-armed bandit), and track business metrics to inform decision-making. Build ML Solutions: Develop models to optimize customer segmentation, retention, conversation quality, and revenue growth. Mentor & Guide: Serve as the team’s AI/ML authority, overseeing advanced statistical work and model development. Ensure Model Excellence: Validate, test, and refine models for performance, scalability, and real-world applicability. What We’re Looking For Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field. Experience: 2-5 years in data science, machine learning, or product analytics with hands-on model development. Technical Expertise: Proficiency in Python and SQL (BigQuery preferred). Mastery of machine learning techniques: regression, classification, clustering, time series analysis. Hands-on experience with frameworks like XGBoost, Naive Bayes, K-means, Neural Networks. Familiarity with ML libraries (TensorFlow, PyTorch, Keras). Strong grasp of statistical analysis (descriptive and inferential). Bonus Skills: Exposure to NLP, computer vision, or reinforcement learning. Experience in e-commerce, retail analytics, or product analytics. Knowledge of CI/CD pipelines for ML deployment. Familiarity with large language models (LLMs) and prompt engineering. Why Apply? Solve High-Impact Problems: Your work will shape strategies that directly affect business growth and customer experiences. Innovate with AI: Work at the forefront of AI/ML applications in a dynamic, collaborative environment. Ownership & Growth: Lead projects from ideation to deployment while mentoring peers and refining best practices. Note: Mergen Partners values diversity and is proud to support our client in building an inclusive team. All qualified applicants will receive consideration. Show more Show less

To provide data science and quantitative analytical support for PSPV by ensuring timely delivery of high-quality reports to meet regulatory requirements, enable medical safety analysis and monitor safety profile of Novartis products. Build capabilities to leverage the Science of data to enhance safety surveillance, case management and delivery of management metrics. These tasks are to be performed independently. Responsible for independently managing small to medium Projects. Major Accountabilities: Engineer key analytical insights into simple, intuitive, and we'll-designed tools and ser vices tailored for each role and process in areas such as: aggregate analysis and reports including PSUR, DSUR, PQR, APR and audit/inspec- tion related outputs PV leaning analytics budget forecasting resource allocation and vendor management support patient focused protocol design and reported outcome. risk-based operational monitoring of country operations detecting data anomalies, outliers and procedural errors signal detection track the business s performance against data analysis model and monitor trends in key business KPIs, providing valuable insights to relevant departments for overall business performance improvement. Provide solutions for a variety of business applications including but not limited to: Customer Segmentation Targeting, Event Prediction, Propensity Modelling, Churn Modelling, Customer Lifetime Value Estimation, Forecasting, Recommender Systems, Modelling Response, Optimization Collaborates with globally dispersed internal stakeholders and cross-functional teams to solve critical business problems, drive operational efficiencies, and deliver successfully on high visibility strategic initiatives. Interact with domain experts to understand the data and the business challenges and design solutions to the challenges using data. Ability to translate business questions into analytical problems and build solutions using visualization techniques, machine learning, statistical modeling. Independently manage small to medium projects. Ability to provide understandable and actionable business intelligence for key stakeholders. Research and develop new algorithms, methods, statistical models and business models and providing insight into structured and unstructured data. Working with the internal process experts and key stakeholders in providing analysis and interpretation of safety/PV data. Is responsible in creation of new data sciences capabilities for the business by envisioning and executing strategies that will influence improvement of the business s performance by enabling informed decision making. Collaborate with partners in IT and Development functions to improve the availability of data (near-real time data flows) and data quality as required for high quality, innovative, analytical/ technical solutions. Bring in efficiency with innovative solutions; effective usage of new technologies and con- cepts; developing new analysis opportunities by integrating existing and new data sources. Continuously monitor external environment to ensure that the organisation can leverage key developments in data science and AI. Lead the design and oversees the development of predictive and data-driven solutions (for ex: SafetyReporting tool) and services to ensure drug, device, trial and patient level benefit/risk information is available proactively for safety analysis, signal detection and risk management. Manage timely delivery of high quality PVMedical safety listing, analysis and data and ensure compliance with health authority regulations. Supports and represents Dats Science team in regulatory inspections/audits, as and when required. Should mentor and coach data science leads, by guiding them through the execution of their duties, and encouraging their professional growth in preparation for their occupation of his position in future. Act as deputy of Sr. Manager Data Science and AI/Head Data Science/Head Data Analytics as and when needed. Minimum requirements : Masters/bachelors Degree in Data Science, Statistics, Math- ematics, Operations Research, Engineering (Biotech, Com- puter Science, Information Technology), Pharmaceutical, Life Science or similar field. MBA/PhD from reputed institutes can also apply. 10-15 years of experience in Data Science related fields with specific experience of producing Key Performance Indicators or similar monthly reports for Senior Management and build- ing statistical models for addressing business issues 1-3 years of Pharma, CRO, Life Science experience would be preferred. Hands on technical expertise in at least 1 of : Machine Learning, Deep Learning, Text Mining and Natural Language Processing, Time Series, Image processing, Neural Networks. Ability to work independently to be able to design, develope code and manage algorithms / Machine Learning models in Python/R/Java Advance level skills in Python/ R is a must, and working knowledge in SQL - ability to write and understand queries and extract complex datasets and produce reports from Databases (including Oracle, SAS, non-relational databases) Pharmacovigilance/Clinical operations domain expertice. Ability to translate technical concepts for non-technical users in the areas of clinical databases (both internal and external). Extensive experience in working with large-scale datasets (in big data architecture, data lake, data mart, data warehouse) and be able to preprocess the data as per the requirement. Ability to work in teams, manage and coordinate projects, under pressure, demonstrating initiative and flexibility through effective and innovative technical ability. Interpersonal and communication skills, and ability to operate effectively in a matrix environment Attention to detail, quality, time management and customer focus

The Grab Integrity team is dedicated to protecting the Grab platform from multiple types of fraud and safety incidents. Our team uses rich datasets ranging from payment risk prediction using sequence-based models to detecting money laundering with graph algorithms and ensuring platform safety. we'research new methods to stay ahead of latest fraud tactics, contributing to the creation of thoughtful and secure products. Get to Know the Role: you'll fight fraud by analysing transactional data, developing and deploying machine learning models, and collaborating with teams to ensure seamless integration of fraud detection systems. you'll help keep our platform safe and trustworthy. The Critical Tasks You Will Perform: Collaborate with your team to understand and convert operational issues into data science problems, aligning efforts with our strategic goals. Stay updated with the latest research and advancements in the field, incorporating the latest models and techniques to address new fraud tactics. Handle data preparation and augmentation, using multiple data types to create comprehensive datasets for model training. Train and improve machine learning models, ensuring accuracy and efficiency in fraud detection through careful selection and tuning of algorithms. Types of algorithms the team work on include Graph Neural Networks, Transformer/ Sequence models, finetuned LLMs, Boosted Trees Deploy models into production, managing their performance, and working with data scientists, software engineers, and product managers to ensure seamless integration and ongoing improvements. The Essential Skills You Will Need: Degree in computer science, physics, statistics, or a related quantitative field. Proficiency in Python, SQL, and programming skills, with familiarity with numeric libraries, containers, and modular software design 2 or more years of experience of standard machine learning libraries such as TensorFlow, PyTorch, XGBoost, LightGBM, and Scikit-learn Understand and some experience using traditional ML techniques like Boosted Trees, and deep neural network architectures, like CNNs, RNNs, Transformers. We care about your we'll-being at Grab, here are some of the global benefits we offer: We have your back with Term Life Insurance and comprehensive Medical Insurance. With GrabFlex, create a benefits package that suits your needs and aspirations. Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through lifes challenges

Key Responsibilities: Design, develop, and optimize NLP models for text classification, sentiment analysis, chatbots, and language generation. Work with deep learning models such as Transformers (BERT, GPT, LSTMs) to enhance NLP capabilities. Implement and fine-tune machine learning algorithms for text analytics and information extraction. Collaborate with data engineers and software developers to integrate NLP solutions into real-world applications.

You will be the Data Scientist in the Fraud Risk team , where you will work on leading new projects to build and improve the Risk strategies to prevent fraud using the Risk tooled and custom data AL/ML models. In this position, you will be partnering with the corresponding Business Units to align with and influence their strategic priorities, educate business partners about Risk management principles, and collaboratively optimize the Risk treatments and experiences for these unique products and partners. Your day to day In this role you will have full ownership of a portfolio of merchants and is responsible for end-to-end management of loss and decline rates. Collaborate with different teams to develop strategies for fraud prevention, loss savings, and optimize transaction declines or improve customer friction. You will work together with cross-functional teams to deliver solutions and providing Risk analytics on frustration trend/ KPIs monitoring or alerting for fraud events. These solutions will adapt PayPal s advanced proprietary fraud prevention tools enabling business growth. What do you need to bring 3-6 years of relevant experience working with large-scale complex dataset. Strong analytical mindset, ability to decompose business requirements into an analytical plan, and execute the plan to answer those business questions Excellent communication skills, equally adept at working with engineers as we'll as business leaders Want to build new solutions and invent new approaches to big, ambiguous, critical problems Strong working knowledge of Excel, SQL and Python/R Technical Proficiency: Exploratory Data Analysis and expertise in preparing a clean and structured data for model development. Experience in applying AI/ML techniques for business decisioning including supervised and unsupervised learning (eg, regression, classification, clustering, decision trees, anomaly detection, etc). Knowledge of model evaluation techniques such as Precision, Recall, ROC-AUC Curve, etc along with basic statistical concepts

As a Data Scientist, you will be assisting with designing and developing cutting-edge AI/ML solutions across the organization. Knowledge / Experience Basic working experience in data analysis and ML modeling. Understanding of machine learning and deep learning concepts. Ability to assist in formulating business problems as machine learning problems. Basic knowledge of model evaluation and interpretability. Familiarity with computer vision, natural language processing, and anomaly detection. Awareness of GenAI and its applications in the industry. Good communication skills: Ability to communicate ideas clearly and concisely, both verbally and in writing. Ability to collaborate effectively with business and tech teams. Problem-solving skills with a focus on optimal solutions. Knowledge of the financial services marketplace is a plus. Skills Proficiency in Python. Good-to-have: Experience in additional programming languages (eg, Java or C#). Familiarity with frameworks and libraries like scikit-learn, TensorFlow, PyTorch, Keras, pandas, etc Basic knowledge of REST services using Flask or FastAPI. Understanding of SQLAlchemy and Pydantic data modeling. Experience with one of the cloud platforms (eg, AWS, GCP, Azure) and AI Studios is a plus. Basic understanding of solving practical problems using GenAI. Familiarity with leveraging LLMs for practical solutions. Team skills, including the ability to establish and maintain effective working relationships both internally and externally. Experience working with at least one relational database. Basic experience in application development and using IDEs. Innovative thinking. Qualifications bachelors degree in engineering or related field or the equivalent combination of education, training, or work experience. Degree / Certification in Data science, Statistics, or related fields. Competencies Fluent in English Excellent communicator - ability to discuss technical and commercial solutions to internal and external parties and adapt depending on the technical or business focus of the discussion. Attention to detail - track record of authoring high quality documentation. Organized approach - manage and adapt priorities according to client and internal requirements. Self-starter but team mindset - work autonomously and as part of a global team What we offer you A multifaceted job with a high degree of responsibility and a broad spectrum of opportunities A broad range of professional education and personal development possibilities - FIS is your final career step! A competitive salary and benefits A variety of career development tools, resources, and opportunities

Participate in client engagements in enhancing current statistical models and develop new models for business needs or design of experiments for test/control analysis and business process trials. Consult with cross-functional teams on matters relating to Machine Learning, knowledge discover, data modeling, and analytics. Own predictive models/ML and DOE/A-B testing areas of production ready ML models hosted across multiple systems. Develop business context for environment and ML uses/applications and deep knowledge of data inputs, outputs, and statistical testing/modeling Write/Run data extraction algorithms to acquire data from primary or secondary data sources and ability to describe/direct data requests for representative data necessary for analyses Develop statistical tests and predictive solutions to make business recommendations for decisioning Train/develop models, run evaluation experiments, and perform statistical analysis of results, refine and test / validate models in production Develop understanding of data framework and how it relates to business use, specific process time points, and make recommendations for any new data needs. Coordinate with data engineers to ensure data is representative of analysis solutions. Use of data analytics and other strategies that optimize statistical efficiency and quality Interpret data, analyze results using statistical techniques and provide ongoing reports Identify, analyze, and interpret trends or patterns in complex data sets Work with management to prioritize business and information needs Perform Ad Hoc Data Analysis and reporting for model performance Locate and define new process improvement opportunities for testing and predictive modeling Qualifications Required / Desired Skills Advanced degree in one or more quantitative discipline Operations Research, Stats, Math, Comp Sci, Engineering, Economics or similar Experience in developing a variety of machine learning models algorithms in a commercial environment with a track record of creating meaningful business impact. Proficient with Pyspark, noSQL and Python and distributed programming Expertise working in MongoDb, Snowflake, Databricks and cloud computing platforms (AWS, GCP or Azure), or equivalent on-premise platform and deployment Experience in client engagements, interpreting client s business challenges, and recommendations for statistical analysis solutions (ie analytical consulting and solution design) Experience in presentation design, development, delivery, and communication skills to present analytical results and recommendations for action-oriented data driven decisions and associated operational and financial impacts. Experience in Gen AI, LLM Workflow, Graph RAG etc is an added advantage. Flexible to work in shift model.

The Client Services BI Analytics team strives to create an open, trusting data culture where the cost of curiosity - the number of steps, amount of time, and complexity of effort needed to use operational data to derive insights - is as low as possible. We govern Client Services operational data and metrics, create easily usable dashboards and data sources, and analyze data to share insights. We are a part of the Client Services Global Business Operations function and work with all levels of stakeholders, from executive leaders sharing insights with the C-Suite to customer-facing colleagues who rely on our assets to incorporate data into their daily responsibilities. This specialist role makes data available from new sources, builds robust data models, creates and optimizes data enrichment pipelines, and provides engineering support to specific projects. You will partner with our Data Visualizers and Solution Designers to ensure that data needed by the business is available and accurate and to develop certified data sets. This technical lead and architect role is a force multiplier to our Visualizers, Analysts, and other data users across Client Services. Responsibilities Design, develop, and maintain scalable data pipelines and systems. Monitor and troubleshoot data pipeline issues to ensure seamless data flow. Establish data processes and automation based on business and technology requirements, leveraging Visa s supported data platforms and tools Deliver small to large data engineering and Machine learning projects either individually or as part of a project team Setup ML Ops pipelines to Productionalize ML models and setting up Gen AI pipelines Collaborate with cross-functional teams to understand data requirements and ensure data quality, with a focus on implementing data validation and data quality checks at various stages of the pipeline Provide expertise in data warehousing, ETL, and data modeling to support data-driven decision making, with a strong understanding of best practices in data pipeline design and performance optimization Extract and manipulate large datasets using standard tools such as Hadoop (Hive), Spark, Python (pandas, NumPy), Presto, and SQL Develop data solutions using Agile principles Provide ongoing production support Communicate complex concepts in a clear and effective manner Stay up to date with the latest data engineering trends and technologies to ensure the companys data infrastructure is always state-of-the-art, with an understanding of best practices in cloud-based data engineering This is a remote position. A remote position does not require job duties be performed within proximity of a Visa office location. Remote positions may be required to be present at a Visa office with scheduled notice. Primary / Basic Skills 3 or more years of work experience with a Bachelor s Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) 3+ years of work experience with a bachelor s degree in the STEM field. Strong experience with SQL, Python, Hadoop, Spark, Hive, Airflow and MPP data bases Experience with both traditional data warehousing tools and techniques (such as SSIS, ODI, and on-prem SQL Server, Oracle) as well as modern technologies (such as Hadoop, Denodo, Spark, Airflow, and Python) Advanced knowledge of SQL (e.g., understands subqueries, self-joining tables, stored procedures, can read an execution plan, SQL tuning, etc.) Solid understanding of best practices in data warehousing, ETL, data modeling, and data architecture. A team player and collaborator, able to work well with a diverse group of individuals in a matrixed environment Preferred Skills A solid understanding of best practices in data engineering Strong analytics experience with a focus on Data Engineering and AI Experience with cloud-based data warehousing and data pipeline management (AWS, GCP, Azure) Experience with NoSQL databases (e.g., MongoDB, Cassandra) Maintainance and Support of Data pipelines (daily / monthly) Experience with visualization software (e.g., Tableau, QlikView, PowerBI) is a plus.

Key Responsibilities: Design, develop, and optimize NLP models for text classification, sentiment analysis, chatbots, and language generation. Work with deep learning models such as Transformers (BERT, GPT, LSTMs) to enhance NLP capabilities. Implement and fine-tune machine learning algorithms for text analytics and information extraction. Collaborate with data engineers and software developers to integrate NLP solutions into real-world applications.

Key Responsibilities: Design, develop, and optimize NLP models for text classification, sentiment analysis, chatbots, and language generation. Work with deep learning models such as Transformers (BERT, GPT, LSTMs) to enhance NLP capabilities. Implement and fine-tune machine learning algorithms for text analytics and information extraction. Collaborate with data engineers and software developers to integrate NLP solutions into real-world applications.

ABOUT THE ROLE The Global Quality Analytics and Innovation team leads the digital transformation and innovation effort throughout Amgen’s Quality organization. We are at the forefront of developing and rolling out data-centric digital tools, employing automation, artificial intelligence (AI), and generative AI to drive end-to-end quality transformation. We are seeking a highly motivated and experienced Senior Data Scientist with a strong background in Generative AI, Large Language Models (LLMs), and MLOps, along with an understanding for Quality in regulated environments (e.g., GxP). This role will play a key part in designing, developing, and deploying scalable AI/ML solutions to drive innovation, efficiency, and regulatory compliance across the organization. You will collaborate with cross-functional teams, including software engineers, data engineers, business stakeholders, and quality professionals to deliver AI-driven capabilities that support strategic business objectives. The ideal candidate is an analytical thinker with excellent technical depth, communication skills, and the ability to thrive in a fast-paced, agile environment. Key Responsibilities Design, build, and deploy generative AI and LLM-based applications using frameworks such as LangChain, LlamaIndex, and others. Engineer reusable and effective prompts for LLMs like OpenAI GPT-4, Anthropic Claude, etc. Develop and maintain evaluation metrics and frameworks for prompt engineering. Collaborate with business stakeholders to identify AI/ML opportunities, ensuring alignment between technical solutions and business goals. Lead the development of MLOps pipelines for model deployment, monitoring, and lifecycle management. Conduct data quality assessments, data cleansing, and ingestion of unstructured documents into vector databases. Build retrieval algorithms for relevant data identification to support LLMs and AI applications. Ensure AI/ML development complies with GxP and other regulatory standards, fostering a strong Quality culture. Partner with global and local teams to support regulatory inspection readiness and future technological capabilities in AI. Share insights and findings with team members in an Agile (SAFe) environment. Basic Qualifications Doctorate degree OR Master’s degree and 4–6 years of experience in Software Engineering, Data Science, or ML Engineering OR Bachelor’s degree and 6–8 years of experience OR Diploma and 10–12 years of experience Preferred Qualifications Proven experience developing and deploying LLM applications. Strong foundation in ML algorithms, data science workflows, and NLP. Expertise in Python and ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn). Familiarity with MLOps tools (e.g., MLflow, CI/CD, version control). Experience with cloud platforms (AWS, Azure, GCP) and tools like Spark, Databricks. Understanding of RESTful APIs and frameworks like FastAPI. Experience with BI and visualization tools (e.g., Tableau, Streamlit, Dash). Knowledge of GxP compliance and experience working in regulated environments. Domain experience in healthcare, biotech, or life sciences is a plus. Strong communication skills with the ability to explain complex topics to diverse audiences. High degree of initiative, self-motivation, and ability to work in global teams.

Your tasks As a team member of the Group Sector Central Function Technology of ContiTech, you will be globally responsible to drive success of applied data science and effective usage in all areas of the organization. Design and implement innovative data-drive solutions to solve complex business problems, using cutting-edge techniques to build up customer centric platforms to reduce complexity, ensure standards, maximize cost savings as well as developing innovative material-driven solutions for a carbon-neutral and 100% circular product portfolio. You Consult on effectively applying Data Science methodology, technology, and good practices, complementing AI/DS platform owners, AI/DS sector owners, AI/DS project owners. Collaborates with stakeholders to identify data and AI opportunities, translates business needs into actionable data requirements and success metrics, and drives prioritization of use cases based on feasibility and impact . Develop best practices, guidelines, and standards for data science in collaboration with AI/DS stakeholders to build solutions. Maintain a deep understanding of the business and industry to identify emerging trends and research to actively drive technology opportunities. Work closely with cross-functional teams to identify opportunities for improving business performance and delivering impactful results. Develop and implement models and algorithms using machine learning, deep learning, natural language processing, and statistical analysis. Lead the development and implementation of data pipelines and infrastructure to support data analysis and modeling. Establish and maintain partnerships with internal and external partners. Optimize data-driven decision-making processes to enhance operational efficiency and effectiveness. Ensure data quality and integrity through rigorous validation and testing procedures. Your Qualifications University degree in Computer Science, Data Science, Mathematics, Statistics, or related fields. Extensive knowledge of data science techniques including descriptive and inferential statistics, big data and data mining technologies, and machine learning. Extensive experience in Data Science, AI, and Machine Learning, with a solid understanding of algorithms, predictive models, deep learning, and natural language processing (NLP). Strong technical proficiency in programming languages and tools (Python, R, TensorFlow, PyTorch, etc.) More than 5 years of dedicated experience in various data science-related roles in the industry. Excellent communication and leadership skills. Critical thinking, organizational skills, and a problem-solving mindset. Experience in a manufacturing industry is a plus. Inspire your stakeholders and contact partners with effective communication and high intercultural sensitivity. Collaborative, team player attitude and convinced that networking and knowledge sharing are key drivers for success. Up-to-date on the latest trends in the industry concerning classical data science, data mining, advanced analytics, etc. Proven ability to translate complex data into actionable insights for non-technical stakeholders. Demonstrated ability to work in a fast-paced, dynamic environment and adapt to changing priorities.

Job Description Summary What you need to know about the role Each Data Scientist on this team has full ownership of a portfolio of a product and is responsible for end-to-end management of loss and decline rates. Day-to-day duties include data analysis, monitoring and forecasting, creating the logic for and implementing risk rules and strategies, providing requirements to data scientists and technology teams on attribute, model and platform requirements, and communicating with global stakeholders to ensure we deliver the best possible customer experience while meeting loss rate targets. Meet our team PayPals Global Fraud Protection team is responsible for partnering with global business units to manage a variety of risk of various types, including identity fraud, account takeover, stolen financial fraud, and credit issues. This is an exciting department that plays an important role in contributing PayPals bottom line financial savings, ensuring safe and secure global business growth, and delivering the best customer experience. This open opportunity is within the Large Merchant and Markets Fraud Risk team. This portfolio is comprised of PayPal s newest leading-edge payments solutions, such as Risk-as-Service, Fastlane, PayPal Complete Payments, etc. as well as customized experiences developed for the company s highest-priority strategic Markets and Partnerships. Job Description Your way to impact You will be the Data Scientist in the Fraud Risk team , where you will work on leading new projects to build and improve the Risk strategies to prevent fraud using the Risk tooled and custom data AL/ML models. In this position, you will be partnering with the corresponding Business Units to align with and influence their strategic priorities, educate business partners about Risk management principles, and collaboratively optimize the Risk treatments and experiences for these unique products and partners. Your day to day In this role you will have full ownership of a portfolio of merchants and is responsible for end-to-end management of loss and decline rates. Collaborate with different teams to develop strategies for fraud prevention, loss savings, and optimize transaction declines or improve customer friction. You will work together with cross-functional teams to deliver solutions and providing Risk analytics on frustration trend/ KPIs monitoring or alerting for fraud events. These solutions will adapt PayPal s advanced proprietary fraud prevention tools enabling business growth. What do you need to bring 3-6 years of relevant experience working with large-scale complex dataset. Strong analytical mindset, ability to decompose business requirements into an analytical plan, and execute the plan to answer those business questions Excellent communication skills, equally adept at working with engineers as well as business leaders Want to build new solutions and invent new approaches to big, ambiguous, critical problems Strong working knowledge of Excel, SQL and Python/R Technical Proficiency Exploratory Data Analysis and expertise in preparing a clean and structured data for model development. Experience in applying AI/ML techniques for business decisioning including supervised and unsupervised learning (e.g., regression, classification, clustering, decision trees, anomaly detection, etc.). Knowledge of model evaluation techniques such as Precision, Recall, ROC-AUC Curve, etc. along with basic statistical concepts Additional Job Description Subsidiary PayPal Travel Percent 0 For the majority of employees, PayPals balanced hybrid work model offers 3 days in the office for effective in-person collaboration and 2 days at your choice of either the PayPal office or your home workspace, ensuring that you equally have the benefits and conveniences of both locations. Our Benefits We have great benefits including a flexible work environment, employee shares options, health and life insurance and more. To learn more about our benefits please visit https//www.paypalbenefits.com . Who We Are Click Here to learn more about our culture and community. Commitment to Diversity and Inclusion PayPal provides equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, pregnancy, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state, or local law. In addition, PayPal will provide reasonable accommodations for qualified individuals with disabilities. . Belonging at PayPal Our employees are central to advancing our mission, and we strive to create an environment where everyone can do their best work with a sense of purpose and belonging. Belonging at PayPal means creating a workplace with a sense of acceptance and security where all employees feel included and valued. We are proud to have a diverse workforce reflective of the merchants, consumers, and communities that we serve, and we continue to take tangible actions to cultivate inclusivity and belonging at PayPal. Any general requests for consideration of your skills, please Join our Talent Community . We know the confidence gap and imposter syndrome can get in the way of meeting spectacular candidates. Please don t hesitate to apply.