Data Scientist Machine Learning & Applied Analytics.

Tata Elxsi, Inc.; Bengaluru; On-site; INR 1,500,000 - 2,800,000; Full time

Job summary

Tata Elxsi, Inc. is seeking a seasoned Data Scientist to translate complex product and customer problems into scalable analytics and ML use cases. The role involves exploring data, engineering features, and building validated models for production deployment in SaaS platforms.

You will collaborate with product, architecture and engineering teams to integrate ML capabilities into products, evaluate models, and drive continuous improvement across the analytics lifecycle.

Qualifications

• 5-8 years of relevant experience in Data Science, Machine Learning, Applied Analytics or related roles.
• Experience building ML/analytics capabilities deployed in production products or platforms.
• Experience 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 related quantitative discipline preferred.

Responsibilities

• 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 optimize 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.
• 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.

Skills

Data Science

Machine Learning

Analytics

Experimentation

Model Deployment

Production Systems

SaaS products

Python

TensorFlow

PyTorch

ML Ops

Education

Master's degree in Computer Science/Data Science/Statistics/Engineering

Tools

Python

TensorFlow

PyTorch

Job description

Role & responsibilities

• 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 optimize 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.

Preferred candidate profile

• 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.