4 Time-Series Modeling Jobs

Req ID: 328025 NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now. We are currently seeking a Systems Integration Specialist to join our team in Bangalore, Karntaka (IN-KA), India (IN). Experience: 4-6 years Key Responsibilities: . Build machine learning models to predict asset health, process anomalies, and optimize operations. . Work on sensor data pre-processing, model training, and inference deployment. . Collaborate with simulation engineers for closed-loop systems. Skills Required: . Experience in time-series modeling, regression/classification, and anomaly detection. . Familiarity with Python, scikit-learn, TensorFlow/PyTorch. . Experience with ML Ops on Azure ML, Databricks, or similar. . Understanding of manufacturing KPIs (e.g., OEE, MTBF, cycle time). About NTT DATA NTT DATA is a $30 billion trusted global innovator of business and technology services. We serve 75% of the Fortune Global 100 and are committed to helping clients innovate, optimize and transform for long term success. As a Global Top Employer, we have diverse experts in more than 50 countries and a robust partner ecosystem of established and start-up companies. Our services include business and technology consulting, data and artificial intelligence, industry solutions, as well as the development, implementation and management of applications, infrastructure and connectivity. We are one of the leading providers of digital and AI infrastructure in the world. NTT DATA is a part of NTT Group, which invests over $3.6 billion each year in R&D to help organizations and society move confidently and sustainably into the digital future. Visit us at NTT DATA endeavors to make accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at . This contact information is for accommodation requests only and cannot be used to inquire about the status of applications. NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement, please click . If you'd like more information on your EEO rights under the law, please click . For Pay Transparency information, please click.

To ensure youre set up for success, you will bring the following skillset & experience: You have 4+ years of experience with application development using Python/Fast API, Java, RESTful services, high-performance, and multi-threading. Bachelors or masters degree in computer science, Statistics, Mathematics, Data Science, or a related field. Full Stack Developer with emphasis on [Python, Relational (PostGres) and Vector databases (FAISS, Weaviate, Milvus), Cloud Platforms/Services (AWS, GCP, Azure)] Good to have Frontend Development (Angular, React and SSR) experience Experience with machine learning frameworks and libraries (PyTorch, TensorFlow, scikit-learn). Experience with LangChain, LangGraph and LlamaIndex and LiteLLM for building LLM-powered applications Understanding of time-series modeling, anomaly detection, or clustering techniques. Experience with data engineering (ETL pipelines, data warehouses, etc.) and Hands-on exposure to model tracking (MLflow), serving (FastAPI, vLLM), and container orchestration frameworks (Kubernetes, DockerSwarm)

Develop and implement Media Mix Models to optimize marketing spend across different channels (e.g., TV, digital, radio, print, etc.). Analyze historical data to understand the impact of marketing efforts and determine the effectiveness of different media channels. Collaborate with marketing and business teams to translate business objectives into quantitative analyses and actionable insights. Build predictive models to forecast the impact of future marketing activities and recommend budget allocation. Present and communicate complex findings in a clear, concise, and actionable manner to both technical and non-technical stakeholders. Perform deep-dive analyses of marketing campaigns and customer data to identify trends, opportunities, and areas for improvement. Ensure data integrity, accuracy, and consistency in all analyses and models. Stay up-to-date with the latest trends and advancements in media mix modeling, marketing analytics, and data science. Collaborate with cross-functional teams including Data Engineering, Marketing, and Business Intelligence to ensure seamless data flow and integration. Create and maintain documentation for all models, methodologies, and analysis processes. Qualifications Bachelors or Masters degree in Data Science, Statistics, Economics, Mathematics, or a related field. Proven experience (6+ years) working in Media Mix Modeling (MMM) and/or marketing analytics. Strong proficiency in statistical modeling techniques (e.g., regression analysis, time-series modeling) and data analysis. Hands-on experience with tools and technologies such as Python, R, SQL, and data visualization platforms (e.g., Tableau, Power BI). Familiarity with marketing data sources (e.g., Nielsen, IRI, social media data, CRM, etc.). Excellent problem-solving skills and a strong analytical mindset. Ability to translate complex data into actionable insights and recommendations for business stakeholders. Strong communication skills with the ability to present findings to both technical and non-technical audiences. Experience working in a fast-paced, data-driven environment. Familiarity with machine learning techniques and frameworks is a plus. Preferred Qualifications: Experience working with large datasets and cloud-based data platforms (e.g., AWS, Azure, Google Cloud). Knowledge of marketing attribution models, customer segmentation, and lifetime value (LTV) analysis. Experience in running A/B tests and controlled experiments. Prior experience in a consulting or marketing agency environment is a plus.

Senior Data Scientist Location: Onsite Bangalore Experience: 8+ years Role Overview We are seeking a Senior Data Scientist with a strong foundation in machine learning, deep learning, and statistical modeling, with the ability to translate complex operational problems into scalable AI/ML solutions. In addition to core data science responsibilities, the role involves building production-ready backends in Python and contributing to end-to-end model lifecycle management. Exposure to computer vision is a plus, especially for industrial use cases like identification, intrusion detection, and anomaly detection. Key Responsibilities Develop, validate, and deploy machine learning and deep learning models for forecasting, classification, anomaly detection, and operational optimization Build backend APIs using Python (FastAPI, Flask) to serve ML/DL models in production environments Apply advanced computer vision models (e.g., YOLO, Faster R-CNN) to object detection, intrusion detection, and visual monitoring tasks Translate business problems into analytical frameworks and data science solutions Work with data engineering and DevOps teams to operationalize and monitor models at scale Collaborate with product, domain experts, and engineering teams to iterate on solution design Contribute to technical documentation, model explainability, and reproducibility practices Required Skills Strong proficiency in Python for data science and backend development Experience with ML/DL libraries such as scikit-learn, TensorFlow, or PyTorch Solid knowledge of time-series modeling, forecasting techniques, and anomaly detection Experience building and deploying APIs for model serving (FastAPI, Flask) Familiarity with real-time data pipelines using Kafka, Spark, or similar tools Strong understanding of model validation, feature engineering, and performance tuning Ability to work with SQL and NoSQL databases, and large-scale datasets Good communication skills and stakeholder engagement experience Good to Have Experience with ML model deployment tools (MLflow, Docker, Airflow) Understanding of MLOps and continuous model delivery practices Background in aviation, logistics, manufacturing, or other industrial domains Familiarity with edge deployment and optimization of vision models Qualifications Masters or PhD in Data Science, Computer Science, Applied Mathematics, or related field 7+ years of experience in machine learning and data science, including end-to-end deployment of models in production