About the Role :

We are looking for a Data Scientist with experience in building and deploying machine learning models focused on behavioral signals, risk analytics, fraud detection, and underwriting. The ideal candidate should have experience working with large-scale customer, transaction, and lending datasets to derive actionable insights and improve decision-making across risk and credit functions.

Candidates from BFSI, FinTech, Payments, Digital Lending, NBFCs, or Banking organizations will be preferred.

Key Responsibilities :

- Develop and deploy predictive models for credit risk, underwriting, fraud detection, and customer behavior analysis.

- Identify and engineer behavioral signals from customer, transaction, device, and engagement data.

- Build risk scorecards and machine learning models to improve underwriting and credit decisioning.

- Analyze large datasets to identify trends, patterns, and risk indicators.

- Collaborate with Product, Risk, Credit, and Business teams to translate business problems into data science solutions.

- Design and evaluate model performance using statistical techniques and machine learning methodologies.

- Conduct A/B testing and model validation exercises.

- Monitor model performance and recommend enhancements to improve prediction accuracy.

- Develop dashboards, reports, and insights to support business decision-making.

- Ensure data quality, governance, and compliance with regulatory requirements.

Required Skills & Qualifications :

- 2 - 4 years of hands-on experience in Data Science, Machine Learning.

- Strong understanding of behavioral signals, risk modeling, underwriting, fraud analytics, or credit scoring.

- Experience working with customer, transactional, lending, or financial datasets.

- Proficiency in Python and SQL.

- Strong knowledge of machine learning algorithms, statistical modeling, and predictive analytics.

- Experience with data manipulation and analysis libraries such as Pandas, NumPy, Scikit-learn, and related tools.

- Experience building and evaluating classification, regression, and risk prediction models.

- Strong analytical and problem-solving skills.

Preferred Background :

- Experience in BFSI, FinTech, Payments, Banking, NBFCs, Digital Lending, or InsurTech organizations.

- Exposure to credit underwriting, loan origination, collections analytics, or fraud detection.

- Experience working with alternative data sources and behavioral data signals.

- Familiarity with cloud platforms and modern data ecosystems is a plus.