4 Aiml Models Jobs

You will be combining your expertise in mathematics, statistics, computer science, and domain knowledge to create AIML models that can effectively address various business challenges. Your role will involve collaborating closely with the AI Technical Manager, GCC Petro technical professionals, and data engineers to integrate these models into the business framework. You will be responsible for identifying and framing opportunities to apply advanced analytics modeling and related technologies to data, thereby helping businesses gain insight, improve decision-making, workflow, and automation. In this position, you will need to understand and communicate the value of proposed opportunities with team members and other stakeholders. Identifying the necessary data and appropriate technology to solve the identified business challenges will be a crucial aspect of your responsibilities. You will also be involved in cleaning data, developing, and testing models, as well as establishing the life cycle management process for these models. Additionally, you will provide technical mentoring in modeling and analytics technologies, the specifics of the modeling process, and general consulting skills. Driving innovation in AIML to enhance capabilities in data-driven decision-making will be a key focus area. You are expected to align with the team on shared goals and outcomes, recognize others" contributions, work collaboratively, seek diverse perspectives, and take actions to develop yourself and others beyond existing skill sets. Furthermore, you should encourage innovative ideas, adapt to change and changing technologies, and effectively understand and communicate data insights and model behaviors to stakeholders with varying levels of technical expertise. **Required Qualifications:** - Minimum 5 years of experience in designing and developing AIML models and/or various optimization algorithms (5 to 9 years of experience) - Solid foundation in mathematics, probability, and statistics with demonstrated depth of knowledge and experience in advanced analytics and data science methodologies (e.g., supervised and unsupervised learning, statistics, data science model development) - Proficiency in Python and working knowledge of cloud AIML services; Azure Machine Learning and Databricks preferred - Domain knowledge relevant to the energy sector and working knowledge of the Oil and Gas value chain (e.g., upstream, midstream, or downstream) and associated business workflows - Proven ability to frame data science opportunities, leverage standard foundational tools and Azure services to perform exploratory data analysis for purposes of data cleaning and discovery, visualize data, and identify actions to reach needed results - Ability to quickly assess the current state and apply technical concepts across cross-functional business workflows - Experience with driving successful execution deliverables and accountabilities to meet quality and schedule goals - Ability to translate complex data into actionable insights that drive business value - Demonstrated ability to engage and establish collaborative relationships both inside and outside the immediate workgroup at various organizational levels across functional and geographic boundaries to achieve desired outcomes - Demonstrated ability to adjust behavior based on feedback and provide feedback to others - Team-oriented mindset with effective communication skills and the ability to work collaboratively - Strong problem-solving skills and attention to detail - Excellent communication and collaboration skills,

As the Director of Analytics at one of our portfolio companies in NCR, you will play a crucial role in leading the analytics efforts to drive impactful insights and shape the future of consumer experiences. Your responsibilities will include developing and executing a comprehensive analytics strategy aligned with the company's goals, overseeing the development of advanced analytical models, and implementing state-of-the-art analytics tools and methodologies to enable efficient data exploration and visualization. You will be instrumental in driving the adoption of a data-driven decision-making culture within the organization, collaborating with various teams such as Product, Marketing, Engineering, and others to embed analytics into key decision-making processes. Additionally, you will build, mentor, and lead a diverse team of high-performing data analysts, business intelligence professionals, and data scientists. Your role will also involve providing thought leadership in leveraging AI/ML to develop innovative, data-driven products and solutions, as well as staying updated on industry trends and best practices to continuously enhance the analytics capabilities of the organization. The ideal candidate for this position should have a Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Business Analytics, or a related field. An MBA or equivalent qualification would be a plus. You should possess 8+ years of experience in analytics, business intelligence, or data science, with a proven track record of driving impact in consumer technology, e-commerce, or a similar fast-paced industry. Strong proficiency in data analysis, predictive modeling, and statistical methodologies is essential, along with hands-on experience in data visualization tools such as Tableau and Power BI, as well as programming languages like Python, R, and SQL. Experience in integrating AI/ML models into real-world applications, building and leading high-performing analytics teams, and translating complex analytical findings into actionable business insights are also key requirements for this role. Excellent communication and interpersonal skills are necessary for effective collaboration with both technical and non-technical stakeholders. If you are a visionary leader with a passion for analytics and driving business growth through data-driven insights, we would love to hear from you.,

You will be working at TRST01, a leading Sustainable Tech company based in Hyderabad. TRST01 specializes in innovative solutions for Sustainable Supply Chain management, Automated ESG Reporting, and Climate Action measurement through its digital Measurement, Reporting, and Verification (dMRV) Solution. By leveraging decentralized technology and blockchain, TRST01 delivers reliable data to assist businesses in achieving their ESG goals effectively. As a Data Modeler at TRST01, you will play a crucial role in designing, implementing, and optimizing data models that support carbon accounting, lifecycle analysis, and sustainability reporting. Your responsibilities will include collaborating with cross-functional teams, such as data scientists, software engineers, and sustainability experts, to ensure the integrity, accuracy, and scalability of environmental data models. Key Responsibilities: - Design and develop robust data models for tracking carbon emissions, energy consumption, and sustainability metrics. - Utilize CFRD datasets to establish reliable and scalable data pipelines for climate impact analysis. - Develop entity-relationship diagrams (ERDs) and schema designs to optimize storage and retrieval of climate-related data. - Collaborate with data engineers and scientists to integrate climate and sustainability data into existing platforms. - Implement data validation and quality control measures to ensure accuracy in sustainability reporting. - Support the development of AI/ML models for predictive analysis of carbon reduction strategies. - Ensure compliance with global environmental regulations (such as GHG Protocol, CSRD, and TCFD) in data modeling practices. - Optimize data models for real-time and batch processing of sustainability metrics. - Work with business intelligence teams to develop dashboards and reports based on modeled climate data. - Stay updated with the latest advancements in climate tech, data modeling, and carbon accounting methodologies. Required Qualifications: - Bachelor's or Master's degree in Data Science, Computer Science, Environmental Science, Sustainability, or a related field. - 1-3+ years of experience in data modeling, database design, and schema optimization. - Expertise in CFRD (Carbon Footprint and Reduction Data) and related frameworks. - Strong understanding of relational and non-relational databases (SQL, NoSQL, GraphDB, etc.). - Hands-on experience with big data tools (e.g., Apache Spark, Hadoop) and ETL pipelines. - Proficiency in data modeling tools such as Erwin, Lucidchart, or similar. - Experience working with climate datasets (e.g., satellite imagery, emission inventories, LCA data). - Familiarity with carbon accounting standards like GHG Protocol, SBTi, and ISO 14064. - Strong analytical and problem-solving skills. - Excellent communication and collaboration abilities. Preferred Qualifications: - Experience in cloud-based data solutions (AWS, Azure) for sustainability analytics. - Exposure to machine learning models for climate risk assessment. - Familiarity with GIS-based modeling for environmental impact analysis.,

Job Summary: As part of our AI/ML , you will work closely with data scientists, engineers, and business stakeholders to design, develop, and deploy machine learning models and AI-powered solutions. You will use Dataiku to build robust, scalable, and automated data science workflows that drive business value. Your expertise in AI/ML techniques combined with your experience using Dataiku will be key to transforming data into actionable insights. Key Responsibilities: AI/ML Model Development & Deployment: Design, develop, and deploy machine learning models and AI solutions using Dataiku . Build end-to-end machine learning pipelines that automate data preparation, feature engineering, training, validation, and deployment of models. Leverage Dataikus AutoML capabilities to streamline model building while ensuring model performance. Work with stakeholders to identify and prioritize AI use cases that align with business objectives. Collaboration & Knowledge Sharing: Collaborate with cross-functional teams (data engineers, business analysts, product owners) to integrate machine learning solutions into business workflows. Share insights and knowledge on best practices in data science, machine learning, and the use of Dataiku . Create and maintain detailed documentation for models, pipelines, and workflows within Dataiku . Data Management & Feature Engineering: Work with large datasets, performing feature engineering and transformation to prepare data for machine learning tasks. Utilize Dataiku’s data wrangling capabilities to clean, transform, and organize data for efficient processing. Build reusable data processing workflows within Dataiku to automate recurring tasks and streamline model development. Model Monitoring & Optimization: Continuously monitor and fine-tune deployed models to ensure they deliver high-quality predictions and insights. Implement strategies for model versioning, testing, and monitoring performance over time. Ensure that models adhere to business requirements and ethical AI practices. Innovation & Research: Keep up with the latest trends and advancements in AI/ML techniques and tools. Explore new AI/ML algorithms and methodologies to improve model performance. Contribute to the ongoing evolution of Dataiku workflows to incorporate cutting-edge practices in AI/ML. Required Skills & Qualifications: Experience: 3+ years of experience in machine learning and data science, with a strong focus on deploying models at scale. Hands-on experience working with Dataiku for building, deploying, and automating ML workflows. Proven track record of end-to-end project delivery in AI/ML, including problem definition, model building, validation, and deployment. Technical Skills: Proficiency in Python , R , or similar programming languages used for AI/ML. Solid understanding of machine learning algorithms, statistical modeling, and model evaluation techniques. Experience with Dataiku and its features such as AutoML, visual recipes, and integration with various machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn). Familiarity with cloud environments (AWS, GCP, or Azure) for scalable model deployment. Knowledge of data preprocessing, feature engineering, and optimization techniques. Familiarity with model monitoring, model lifecycle management, and version control in data science projects.