Key Responsibilities :

- Develop, train, and deploy machine learning models to solve business problems (Classification, Regression, Anomaly Detection, Forecasting, etc.).

- Perform data preprocessing, feature engineering, and exploratory data analysis.

- Collaborate with data engineers, product managers, and domain experts to integrate models into production systems.

- Monitor, evaluate, and optimise model performance using industry best practices.

- Contribute to the design and implementation of ML pipelines and MLOps practices.

- Document models, workflows, and key decisions for reproducibility and team knowledge sharing.

- Stay updated with the latest trends in data science, machine learning, and AI.

Required Skills & Experience :

- 2+ years of hands-on experience in data science or applied machine learning roles.

- Proficiency in Statistics, and Linear Algebra is a Must.

- Proficiency in Python and common ML libraries (e.g., scikit-learn, pandas, NumPy, TensorFlow or PyTorch).

- Experience with data wrangling, statistical analysis, and model evaluation.

- Exposure to NLP, computer vision, or deep learning (at least one area).

- Familiarity with CI/CD pipelines and MLOps tools.

- Experience working with relational databases (SQL) and version control (Git, Bitbucket).

- Strong analytical and problem-solving skills.

- Shall be able to go below the hood; and tweak 'existing Code' / 'established Algorithms' to suit given Problem Statements.

- Ability to communicate technical concepts clearly to non-technical stakeholders.

- Experience working in agile or cross-functional teams.

Nice to Have :

- Experience with cloud platforms (AWS, Azure, or GCP).

- Exposure to agentic AI frameworks (LangChain, CrewAI, etc.) or LLMs.

- Knowledge of deployment and automation tools.

- Prior experience in manufacturing, automotive, or enterprise software domains.

- Certifications in Data Science, ML, or Cloud.