Job Description

Introduction to the Role:
We are seeking a highly skilled and passionate Data Scientist to join our Supply Chain & Operations team. You will leverage advanced analytical techniques, machine learning, and statistical modeling to optimize complex supply chain processes, improve operational efficiency, and drive data-driven decision-making across our manufacturing and logistics networks. . This role will help Tata Motors achieve its strategic priorities in enhancing our resilience, reducing costs, and accelerating product flow in a rapidly evolving automotive landscape.
Job roles & responsibilities:
Roles & responsibilities:

• Design , Develop and operationalize AI/ML models for key supply chain & S&OP use cases: like Inventory optimization demand forecasting, network optimization, inventory aging optimization, lead time prediction, and risk detection.
• Utilize advanced statistical methods, machine learning algorithms (e.g., regression, classification, clustering, time series analysis, reinforcement learning), and operations research techniques (e.g., optimization, simulation).
• Conduct in-depth exploratory data analysis to identify trends, anomalies, and opportunities for improvement within supply chain and operational data.
• Identify critical pain points and inefficiencies in existing supply chain and manufacturing processes and propose data-driven solutions.
• Develop and implement strategies for cost reduction, lead time optimization, waste reduction, and quality improvement across the supply chain.
• Contribute to supply chain network design and capacity planning initiatives using data-driven insights..
• Drive the adoption of data science solutions by working collaboratively with operational teams for successful implementation and change management
• Develop and deploy NLP models to extract insights from unstructured text data
• Expertise in statistical analysis, data visualization, and data preprocessing techniques.
• Build and deploy deep learning solutions for time-series, tabular, and unstructured data (e.g., text from contracts, invoices).
• Integrate Generative AI and Agentic AI frameworks (e.g., LangChain, ReAct, Hugging Face Transformers) to create autonomous or semi-autonomous agents that enhance decision-making.
• Collaborate with data engineers and business stakeholders to translate supply chain pain points into production-grade analytics solutions.
• Own the end-to-end ML lifecycle--from data preparation to model validation, deployment, and post-launch performance monitoring.
• Apply optimization and operations research techniques to solve routing, sourcing, and capacity planning problems.

Required Qualifications:

• 5+ years of hands-on experience in data science, with a focus on S&OP , supply chain, manufacturing, or logistics preferred.
• Deep expertise in machine learning (regression, classification, clustering, time-series), deep learning (CNNs, RNNs, Transformers), and reinforcement learning.
• Proven experience with Generative AI (e.g., GPT, diffusion models) and Agentic AI or multi-agent systems.
• Advanced programming skills in Python with experience in ML libraries (PyTorch, TensorFlow, Scikit-learn, XGBoost, Hugging Face, etc.).
• Experience with cloud platforms (AWS/GCP/Azure), containerization (Docker), and deployment pipelines.
• Strong business acumen and the ability to align technical solutions with supply chain goals and KPIs.
• Excellent communication skills with the ability to distill complex ideas for diverse continuously track emerging AI/ML trends and evaluate their relevance to supply chain strategy and automation.
• Coach junior data scientists and shape the internal data science best practices.

Essential & Desirable Education/Qualifications/ Work experience/technical skills i.e. what are the 'must haves' for this role

• Experience with real-time analytics and streaming data (Kafka, Spark).
• Hands-on experience with LangChain, OpenAI API, or ReAct/AutoGPT-style architectures.

• Familiarity with optimization techniques (e.g., MILP, heuristics).

• Knowledge of NLP use cases in supply chain/S&OP --contract parsing, vendor correspondence, part classification.

Education:

• PG/ BE/ B.Tech in Computer Science, Data Science or a related field from a Tier 1 or Tier 2 institution.

• A Master's degree in a relevant field is preferred but not mandatory