Data Scientist: Machine Learning And Applied Analytics.

InterviewPanel; Bengaluru; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

InterviewPanel seeks a Data Scientist – Product Analytics & Machine Learning in Bengaluru. The role involves translating customer problems into scalable analytics and ML solutions across the product portfolio, with responsibilities spanning data exploration, model development, validation, and production monitoring.

The candidate will work with internal analytics teams and external partners to evolve analytics capabilities for SaaS products and platforms. 5–8 years experience required.

Qualifications

• Strong foundation in statistics and ML.
• Hands-on programming in Python with data libraries.
• Experience with structured and time-series data.

Responsibilities

• Translate problems into analytics/ML use cases.
• Perform data exploration, feature engineering and experimentation.
• Develop, train, validate and deploy models.
• Define metrics and benchmarks for model performance.
• Productize prototypes into production capabilities.
• Collaborate with product, architecture and engineering teams.

Skills

Statistics

Python

ML modelling

Time-series analysis

SQL

MLOps

Cloud AWS

GenAI/LLMs

Experimentation

Product analytics

Education

Master's degree in CS/DS/Statistics/Math/Engineering

Tools

Pandas

NumPy

scikit-learn

Job description

Data Scientist Machine Learning & Applied Analytics.

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 help build differentiated analytics and ML 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 will work across the complete lifecycle — from data exploration and experimentation through model development, validation, productization and production monitoring.

The Data Scientist will also work closely with Tata Elxsi's internal analytics teams, specialist solution providers and academic/research institutions to evaluate, integrate and evolve analytical capabilities as part of our products.

Key Responsibilities

Product Analytics & ML

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

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/approaches and provide evidence‑based recommendations.
• Establish practices for model versioning, validation, monitoring, drift detection and continuous improvement.
• Ensure that externally sourced models can be effectively integrated, governed and evolved within the product.

Collaboration & Research

• Collaborate with Tata Elxsi's broader AI, analytics and domain teams and leverage specialist capabilities where required.
• Work technically with external solution providers to understand and integrate their algorithms and models.
• Engage with researchers and academic institutions such as IITs, NITs and IISc on advanced analytics and ML opportunities.
• Evaluate research, algorithms and PoCs and help translate promising approaches into production‑grade product capabilities.
• Build reusable analytical assets, datasets, features, evaluation frameworks and know‑how across the product portfolio.

Technical Skills

• 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 ML frameworks.
• Experience developing predictive models using structured and time‑series data.
• Understanding of supervised/unsupervised learning, anomaly detection, forecasting, classification and regression techniques.
• Experience with feature engineering, model selection, hyperparameter optimisation and model evaluation.
• Good understanding of model explainability, bias, uncertainty and robustness.
• Working knowledge of SQL and large‑scale data processing.
• Experience deploying or integrating ML 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 GenAI/LLMs and their application within product analytics is an advantage.

Product & Engineering Mindset

• Ability to move beyond experimentation and think about how analytics becomes a repeatable product capability.
• Strong problem‑solving ability and comfort working with incomplete or imperfect real‑world datasets.
• Ability to distinguish between an interesting analytical result and one that creates measurable customer/product value.
• Comfortable working closely with Product Managers, Domain SMEs, Architects and Software Engineers.
• Ability to critically evaluate third‑party algorithms and models rather than treating external solutions as black boxes.
• Strong technical communication skills and ability to explain analytical concepts and model outcomes to both technical and business stakeholders.

Preferred Experience

• 5–8 years of relevant experience in Data Science, Machine Learning, Applied Analytics or related roles.
• Experience building ML/analytics capabilities that have been deployed into production products or platforms.
• Experience working with automotive, mobility, connected vehicle, battery/EV, IoT or industrial datasets is desirable but not mandatory.
• Experience collaborating with technology partners, research organizations or academic institutions is an advantage.
• Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related quantitative discipline preferred.

What Success Looks Like

The successful candidate will help PMG establish a strong internal Data Science & ML capability that can build differentiated analytics, intelligently leverage Tata Elxsi's broader capabilities and external specialists, and retain the knowledge and ownership required to continuously evolve analytics as a core part of our products and platforms.