Data Scientist – Product Analytics & Machine Learning

Location: Bengaluru

Experience: 5–8 Years

Role Type: Full-Time

Business Unit: Products & Platforms (PMG)

Role Overview

We are looking for a Data Scientist – Product Analytics & Machine Learning to build differentiated analytics and machine learning capabilities across our product and platform portfolio, including MOBIUS+ and Signate.

This is a hands-on, product-focused role responsible for translating real-world customer and domain problems into scalable analytics and machine learning solutions. The role spans the complete lifecycle, from data exploration and experimentation to model development, validation, productization, and production monitoring.

The Data Scientist will collaborate with internal analytics teams, specialist solution providers, and academic and research institutions to evaluate, integrate, and continuously evolve analytical capabilities as part of our products.

Key Responsibilities

1. Product Analytics & Machine Learning

- Translate product and customer problems into well-defined analytics and machine learning use cases.
- Perform data exploration, feature engineering, statistical analysis, and experimentation.
- Develop, train, validate, and optimize machine learning and predictive models.
- Define appropriate model performance metrics, benchmarks, and acceptance criteria.
- Convert prototypes and experiments into scalable, production-ready capabilities.
- Collaborate with Product, Architecture, and Engineering teams to integrate models and analytics into SaaS products and platforms.

2. Model Evaluation & Productization

- Evaluate models and analytical solutions developed by internal teams, technology partners, and specialist solution providers.
- Assess model assumptions, data requirements, accuracy, robustness, explainability, and limitations.
- Benchmark alternative models and approaches and provide evidence-based recommendations.




- Establish practices for model versioning, validation, monitoring, drift detection, and continuous improvement.
- Ensure externally sourced models can be effectively integrated, governed, and continuously evolved within the product ecosystem.

3. Collaboration & Research

- Collaborate with AI, analytics, and domain teams to leverage specialist capabilities where required.
- Work closely with external solution providers to understand, evaluate, and integrate their algorithms and models.
- Engage with researchers and academic institutions, including IITs, NITs, and IISc, on advanced analytics and machine learning opportunities.
- Evaluate research, algorithms, and proofs of concept (PoCs), translating promising approaches into production-grade product capabilities.
- Build reusable analytical assets, datasets, features, evaluation frameworks, and technical knowledge across the product portfolio.

Technical Skills & Requirements

- Strong foundation in statistics, probability, machine learning, and data analysis.
- Strong hands-on programming skills in Python and common data science libraries.
- Experience with Pandas, NumPy, scikit-learn, and relevant machine learning frameworks.
- Experience developing predictive models using structured and time-series data.
- Understanding of supervised and unsupervised learning, anomaly detection, forecasting, classification, and regression techniques.
- Experience with feature engineering, model selection, hyperparameter optimization, and model evaluation.
- Positive understanding of model explainability, bias, uncertainty, and robustness.
- Working knowledge of SQL and large-scale data processing.
- Experience deploying or integrating machine learning models into production applications through APIs, services, or ML pipelines.
- Understanding of MLOps, including model versioning, experiment tracking, monitoring, and model lifecycle management.
- Experience with cloud environments, preferably AWS, is desirable.
- Exposure to Generative

📌 Data Scientist (Bengaluru)
🏢 Tata Elxsi
📍 Bengaluru