Roles & Responsibility:

We are seeking a Data Scientist with 3+ years of hands-on experience to join our Central Analytics team and drive two mission-critical charters:

- Strategic Intelligence – Build AI-enabled systems that convert unstructured global signals (news, competitor moves, geopolitical events, regulatory shifts, supply-chain disruptions) into executive-ready risk & opportunity insights aligned to Tata Motors' strategic priorities, extending our SPIRO platform.
- Reliability Improvement – Develop advanced statistical and ML models for hazard rate estimation, B10 life prediction, warranty forecasting, and field-failure early-warning — directly targeting IPTV reduction, warranty cost avoidance, and QRT performance improvement.

The role sits at the intersection of survival analysis, generative AI, NLP, and automotive reliability engineering — translating data into measurable business outcomes.

Key Responsibilities

1. Reliability & Survival Analytics

Build discrete-time hazard models using GLM + Complementary Log-Log (CLogLog) link and Natural Cubic Splines for tapered/censored warranty datasets.

Develop B10 / B50 life predictors, MTBF, and Weibull / non-parametric reliability models for engines, gearboxes, aggregates, and BIW structural joints.

Design warranty claim taper matrices, IPTV trend models, and warranty provisioning forecasts integrated with MIS/KIS datasets.

Enable failure-mode–wise reliability analysis (Model / Aggregate / Batch / ABP-wise) with feedback loop into DFMEA, DVP, PFMEA, and supplier Cp/Cpk recommendations.

Collaborate with ERC, Quality, and Manufacturing Engineering to translate insights into design intervention prioritization and ICA/PCA effectiveness feedback.

2. Strategic Intelligence (Risk & Opportunity)

Extend and productionize the SPIRO platform: automated news ingestion (RSS + web), NLP-based content extraction, keyword enrichment, and category exposure mapping (18 categories, 160+ sub-categories).

Implement AI-driven risk & opportunity scoring using FinBERT sentiment, semantic category matching, competitor/supplier detection, and location intelligence.

Build RAG-based "Ask AI" systems using Azure OpenAI for persona-driven executive Q&A; and impact briefs.

Develop competitive intelligence models (perceptual maps, sentiment distribution, strategic movement maps) for product portfolios.

3. ML Engineering & Deployment

Own the end-to-end ML lifecycle — data preparation, feature engineering, model validation, deployment, and monitoring — consistent with Central Analytics MLOps standards.

Deploy models on Azure (Azure ML, Data Factory, Synapse, Blob, App Services) with FastAPI backends and CI/CD via GitHub Actions.





Implement model monitoring, drift detection, and retraining pipelines.

4. Collaboration & Stakeholder Engagement

Work with cross-functional teams — Quality, ERC, Manufacturing, Supply Chain, Strategy, and After-Sales — to convert business problems into analytical formulations.

Present findings to senior leadership in executive-ready formats; contribute to strategic priority reviews (e.g., FY27 Central Analytics roadmap).

Required Qualifications

Category

Requirement

Education

B.E./B.Tech/M.Tech/M.Sc. in Computer Science, Statistics, Applied Mathematics, Industrial Engineering, Mechanical/Automotive Engineering, or related quantitative discipline.

Experience

Minimum 3 years of applied data science experience, preferably in automotive, manufacturing, or industrial reliability domains.

Statistical Modeling

Robust grounding in survival analysis, hazard modeling, GLM, spline-based regression, Weibull/Lognormal distributions, censoring, and Monte Carlo simulation.

Machine Learning

Hands-on experience with regression, classification, clustering, time-series forecasting (ARIMA, Prophet), tree-based models (Random Forest, XGBoost), and anomaly detection.

NLP & Generative AI

Working knowledge of transformers, FinBERT, embeddings, RAG pipelines, LangChain, Azure OpenAI / HuggingFace.

Programming

Proficiency in Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow, FastAPI), SQL, and Git.

Cloud & MLOps

Experience with Azure (preferred) — Data Factory, Synapse, ML Studio, Blob; MLflow, Docker, CI/CD.

Visualization

Power BI / Plotly / Matplotlib for executive dashboards.

Preferred / Nice-to-Have

- Prior exposure to automotive warranty analytics, IPTV, DPV, QRT metrics, DFMEA/PFMEA, or field-failure analysis.
- Familiarity with reliability engineering standards (MIL-HDBK-217, IEC 61508) and Nelson recurrent-event models.
- Experience with graph neural networks, causal inference, or Bayesian methods.
- Exposure to IoT / telematics data (plant + field vehicle data) and edge deployment.
- Contribution to research papers, patents, or open-source in reliability or NLP.

What Success Looks Like (12-Month Outcomes)

- Deployed a production-grade hazard-rate + B10 life prediction model covering at least 2 vehicle platforms,



feeding into warranty provisioning and design intervention prioritization.
- Delivered measurable IPTV reduction / warranty cost avoidance (benchmark: ~35% IPTV reduction and warranty cost reduction of ₹1 Cr+ observed on similar interventions).
- Extended SPIRO with at least 2 new intelligence modules (e.g., supplier risk scoring, regulatory horizon scanning) with LLM-generated executive briefs.
- Established reusable analytics frameworks and reproducible pipelines adopted by peer teams in Central Analytics.

Tata Motors is an Equal Opportunity Employer, committed to a fair and transparent hiring process. All recruitment decisions are based strictly on Merit, Role alignment, and Business requirements. It does not solicit or accept any form of payment or gratuity from candidates at any stage of the recruitment process. Any such request received should be considered fraudulent and reported immediately.

About Tata Motors:

Part of the USD 180 billion Tata group, Tata Motors Limited (BSE: 500570; NSE: TATAMOTORS), a USD 52 billion organization, is a leading global automobile manufacturer of cars, utility vehicles, pick-ups, trucks, and buses, offering an extensive range of integrated, smart, and e-mobility solutions. With ‘Connecting Aspirations’ at the core of its brand promise, Tata Motors is India’s market leader in commercial vehicles and ranks among the top three in the passenger vehicles market.

Tata Motors strives to bring new products that captivate the imagination of GenNext customers, fuelled by state-of-the-art design and R&D; centres located in India, the UK, the US, Italy, and South Korea. By focusing on engineering and tech- enabled automotive solutions catering to the future of mobility, the company’s innovation efforts are focused on developing pioneering technologies that are both sustainable and suited to the evolving market and customer aspirations. The company is pioneering India's Electric Vehicle (EV) transition and driving the shift towards sustainable mobility solutions by developing a tailored product strategy, leveraging the synergy between Group companies and playing an active role in liaising with the Government of India in developing the policy framework.

With operations in India, UK, South Korea, Thailand and Indonesia, Tata Motors markets its vehicles in Africa, the Middle East, Latin America, Southeast Asia, and the SAARC countries. As of March 31, 2025, Tata Motors’ operations include 93 consolidated subsidiaries, two joint operations, four joint ventures, and numerous equity-accounted associates, including their subsidiaries, over which the company exercises significant influence.

📌 Senior Manager-Data Scientist – Strategic Intelligence & Reliability Improvement (Pune)
🏢 Tata Motors
📍 Pune