- Develop and deploy machine learning models for use cases such as credit risk modeling, fraud detection, customer segmentation, churn prediction, and loan underwriting.

- Analyze large, complex datasets to extract actionable insights and improve business performance.

- Collaborate with cross-functional teams, including Risk, Product, Compliance, and Engineering, to deliver impactful data solutions.

- Build scalable data pipelines and implement machine learning solutions to support strategic decision-making.

- Ensure model governance, validation, and regulatory compliance (e.g., Basel norms, IFRS9).

- Communicate findings and insights to stakeholders through dashboards, reports, and presentations.

- Stay updated with industry trends, emerging technologies, and regulatory changes to enhance analytics capabilities.

Preferred candidate profile :

- Bachelors/Masters degree in Data Science, Statistics, Computer Science, Mathematics, or related field.

- 5+ years of experience in Data Science, preferably in Banking/Finance/FinTech.

- Strong expertise in Python/R, SQL, machine learning (supervised & unsupervised), and statistical modeling.

- Hands-on experience with libraries/frameworks such as Scikit-learn, TensorFlow, and PyTorch.

- Experience with big data tools like Spark and Hadoop (preferred).

- Strong understanding of financial concepts, including credit risk, market risk, fraud analytics, and AML.

Preferred qualifications :

- Experience in regulatory modeling (Basel II/III, IFRS9, stress testing).

- Knowledge of cloud platforms (AWS, Azure, GCP).

- Experience with model deployment (MLOps, Docker, Kubernetes).

- Familiarity with BI tools (Tableau, Power BI).

Key competencies :

- Strong analytical and problem-solving skills.

- Excellent communication and stakeholder management abilities.