- Identify AI/ML opportunities from ambiguous or undefined business problems.

- Lead end-to-end model development including data mining, processing, modeling, deployment, monitoring, and continuous improvement.

- Build high-quality datasets through strong data processing and feature engineering practices.

- Conduct exploratory data analysis, statistical inference, and design A/B tests to validate hypotheses.

- Collaborate with cross-functional teams to develop scalable ML solutions and integrate them into production systems.

- Perform model RCA, documentation reviews, and code reviews to ensure reliability and maintainability.

- Develop reusable ML components, frameworks, and best practices.

Required Skills & Experience :

- 48 years of hands-on experience as a Data Scientist or Machine Learning Engineer.

- Advanced degree in a quantitative field (Computer Science, Statistics, Mathematics, Data Science, Engineering) OR strong equivalent industry experience.

- Strong knowledge of the full ML lifecycle : feature engineering, model training, validation, deployment, monitoring, and feedback loops.

- Expertise in Python, SQL, and PySpark for data processing and modeling.

- Experience with data mining, handling large datasets, and working with NoSQL databases.

- Proficiency with cloud platforms like AWS, Azure, or GCP and version control tools like Git.

- Familiarity with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI, etc.).

- Understanding of statistical concepts, EDA, hypothesis testing, and experiment design.

- Must be from Tier 1 Institutes & having experience in fintech or product-based firms.