Sr. Data Scientist

Responsibilities & Key Deliverables

About the Role

Join the Mahindra Group’s dynamic AI Division as a Senior Data Scientist, where you will play a pivotal role in designing and delivering sophisticated AI solutions that propel innovation across diverse sectors including Automotive, Farm Equipment, IT Services, Retail, Logistics, Finance, and Supply Chain Management. This position offers a unique opportunity to contribute to enterprise-grade AI systems that underpin strategic business growth and digital transformation.

Key Responsibilities

• Lead end-to-end machine learning workflows, encompassing problem identification, comprehensive data exploration, innovative feature engineering, rigorous model development, thorough evaluation, and structured documentation to ensure transparency and replicability.
• Develop and refine diverse predictive models including tree-based algorithms, linear models, time-series analytics, and cutting-edge large language models (LLMs), applying strong experimental methods and statistical evaluation frameworks to optimize effectiveness.
• Collaborate seamlessly with cross-functional teams such as data and ML engineers to deploy models into scalable production environments, actively monitor model performance, detect drift, and implement timely updates to maintain accuracy and relevance.
• Deliver quantifiable business impact by driving improvements in key performance indicators including conversion rates, precision-recall metrics, latency, and cost-efficiency targets, ensuring that AI solutions translate into actionable enterprise benefits.
• Promote adoption of best practices in AI ethics, data privacy, and compliance within development processes to uphold Mahindra Group’s standards and social responsibility.
• Mentor junior team members, foster knowledge sharing, and contribute to ongoing skill development within the AI engineering team.

Experience

• 4 to 6 years of hands-on industry experience applying machine learning techniques to solve complex, real-world problems.
• Strong proficiency in Python programming, with expertise in key libraries such as pandas, NumPy, and scikit-learn, along with working knowledge of deep learning frameworks like PyTorch or TensorFlow to build and deploy neural network models.
• Demonstrated expertise in designing and conducting experimentation, including performance evaluation metrics such as AUC and PR-AUC, model calibration, statistical analysis, and cross-validation methodologies to ensure robust and generalizable models.
• Practical experience managing the transition of models from research prototypes within notebooks to production-ready systems, with familiarity in MLOps tools and practices, preferably including MLflow and model registry usage.
• Proven ability to work collaboratively in cross-functional teams including data engineers, product managers, and business stakeholders to translate data insights into impactful machine learning solutions.
• Experience in maintaining model lifecycle, addressing data drift, and implementing continuous improvement processes within production AI systems.

Industry Preferred

Qualifications

Minimum educational qualification:

• Bachelor’s degree in Engineering (B.Tech / BE) or a closely related technical discipline, preferably in Computer Science, Electrical Engineering, or Information Technology.

Additional qualifications that are desirable include:

• Advanced degrees (M.Tech, MS,) in Machine Learning, Data Science, Artificial Intelligence, or related fields.
• Professional certifications in data science, AI, or cloud platforms such as AWS, Azure, or Google Cloud.
• Continuous learning credentials such as courses from recognised platforms to stay current with emerging AI techniques and technologies.

General Requirements

The ideal candidate for this role demonstrates:

• Problem-solving mindset: Ability to identify complex business problems and devise scalable machine learning solutions grounded in rigorous analysis.
• Collaboration and communication: Effective teamwork and clear communication skills to articulate technical concepts to non-technical stakeholders and work across diverse teams.
• Adaptability: Openness to embrace new technologies, methodologies, and dynamic business needs in a fast-evolving AI landscape.
• Attention to detail and quality: Commitment to producing reliable, interpretable models with comprehensive documentation and robust testing.
• Ethical awareness: Consideration of ethical implications of AI models and adherence to privacy and fairness standards.
• Self-motivation: Proactive approach toward continuous learning and taking initiative within projects and team environments