Data Scientist

Role Summary

Join Mahindra AI as a Senior Data Scientist, where your expertise in artificial intelligence and machine learning will be instrumental in transforming vehicle data into actionable insights. You will play a crucial role in creating intelligent systems that assess and predict vehicle resale values, evaluate vehicle condition, and optimize pricing strategies. This position offers an exciting opportunity to apply advanced machine learning techniques to large-scale automotive datasets, driving innovation in the automotive and mobility sectors.

As a key member of our AI Division within Mahindra & Mahindra Ltd, you will collaborate closely with cross-functional teams including product managers, data engineers, and business stakeholders to deliver impactful and scalable solutions. Your work will directly influence strategic business decisions and contribute to enhancing customer experiences in the used vehicle marketplace. This role requires a hands-on approach, combining deep technical expertise with strategic thinking to develop end-to-end AI/ML solutions that are production-ready and impactful.

Responsibilities & Key Deliverables

Core Responsibilities

• Design, implement, and deploy robust AI and machine learning models focused on:
  • Predictive pricing and accurate valuation of pre-owned vehicles based on comprehensive data analysis.
  • Condition assessment of vehicles through advanced sensor data interpretation and inspection report analysis.
• Develop and maintain scalable, end-to-end ML pipelines primarily using Python, ensuring efficient data processing and model execution.
• Handle diverse automotive datasets, including structured data, unstructured textual information, and time-series sensor inputs, facilitating comprehensive model training.
• Collaborate effectively with diverse teams such as product management, data engineering, and business units to drive solutions from concept to production environments.
• Oversee continual model performance monitoring, retrain models using up-to-date data, and optimize algorithms to maintain prediction accuracy over time.
• Create and maintain interactive dashboards and visualizations to communicate analytical insights clearly to stakeholders across technical and non-technical backgrounds.
• Incorporate best practices in MLOps, including model versioning, automated testing, and deployment using containerization technologies.

Essential Skills

• Proficiency in Python programming with solid experience in machine learning frameworks and libraries, such as scikit-learn, XGBoost, CatBoost, TensorFlow, and PyTorch.
• Strong command over data processing and visualization libraries including Pandas, NumPy, Matplotlib, and Seaborn to handle complex datasets and present insights.
• Hands-on experience deploying machine learning models using modern tools such as Docker, FastAPI, and MLflow to ensure seamless integration and scalability.
• Familiarity with cloud computing platforms, including AWS, GCP, or Azure, coupled with a solid understanding of MLOps workflows and automation.
• Expertise in data preprocessing, feature selection and engineering, as well as rigorous model evaluation techniques.
• Demonstrated capability to take full ownership of projects, working independently while driving collaboration with cross-functional teams.

Experience

• Minimum of 3 to 5 years of professional experience in data science roles, specializing in machine learning model development and deployment.
• Experience within automotive analytics, mobility services, or retail pricing sectors is highly valued, reflecting an understanding of domain-specific challenges and datasets.
• Knowledge of emerging technologies such as Generative AI, natural language processing (NLP), and deep learning methods is an advantage, enhancing model sophistication and applicability.
• Proven track record of successfully delivering production-level AI/ML projects that impact business outcomes significantly.
• Exposure to handling large, multi-modal datasets and improving model robustness in dynamic environments.

Qualifications

• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a related technical discipline is required.
• A Master’s or doctoral degree in a relevant field is considered an asset and may substitute for some experience requirements.
• Strong academic foundation in machine learning, statistical modeling, and algorithm development essential for this role.
• Commitment to continuous learning and staying current with advancements in AI and machine learning technologies.