A leading global financial services organization focused on digital lending and credit solutions, operating at the intersection of technology and finance. The company is known for leveraging advanced analytics and data science to drive credit decisioning, risk management, and customer lifecycle optimization.

1. Build and deploy end-to-end credit risk models (application, behavioural, collections).
2. Define data science roadmap and identify high-impact business use cases.
3. Analyse large datasets to drive product optimization and decision-making.
4. Develop and implement machine learning models (regression, random forest, boosting, etc.).
5. Work closely with engineering on data pipelines and model deployment.
6. Perform data exploration, feature engineering, and visualization.
7. Continuously improve models through iteration, monitoring, and debugging in production.
8. Build dashboards to track model performance and business impact.

1. 2–5 years of experience in data science / analytics, preferably in credit risk.
2. Candidates must be from top-tier institutes (IIT/NIT/BITS/IIIT)
3. Strong expertise in Python, SQL, and statistical modeling.
4. Hands-on experience with ML algorithms (SVM, Random Forest, Neural Networks, etc.).
5. Familiarity with data science tools (Pandas, Scikit-learn, TensorFlow, etc.).
6. Experience in credit risk domains (scorecards, loan pricing, collections, forecasting).
7. Exposure to big data tools (Hadoop, Spark) and NoSQL databases is a plus.
8. Strong problem-solving and cross-functional communication skills.

1. Opportunity to work on high-impact credit risk and ML problems at scale.
2. Exposure to end-to-end model lifecycle (build → deploy → monitor).
3. Collaborative environment with data, product, and engineering teams.
4. Strong growth trajectory within a tech-driven financial services setup.