About Applied Data Finance (ADF) Applied Data Finance (ADF) is a technology-driven financial services company focused on B2C lending, leveraging data science, advanced analytics, AI/ML, and technology to build innovative and scalable financial solutions. ADF uses data-driven insights and technology to enhance credit decisioning, risk management, customer experience, and overall lending outcomes.

About the Role We're looking for a Senior Data Scientist to play a key role in shaping our underwriting, pricing, and volume strategy, with a robust focus on unlocking refinancing and repeat lending opportunities for returning customers. This role sits within our Acquisition and Credit Risk Strategy team. You'll use data and analytics to guide lending decisions, risk-based pricing, and responsible portfolio growth - working closely with Data Science, Data Engineering, Product, Collections, Finance, and Compliance to balance growth, profitability, and risk for our lending portfolio.

Responsibilities • Own and develop loan strategy for returning customers - using repayment history and on-book behavior to identify low-risk borrowers eligible for refinance/repeat lending and determine appropriate pricing, offer size, and eligibility.

- Identify and size volume growth opportunities by optimizing cutoffs, expanding into adjacent customer segments, and testing policy changes that increase approvals without disproportionate increases in losses/bad rates.
- Mine cash flow and bank statement data to identify income stability, spending patterns, and repayment capacity signals not captured by traditional bureau data - translating these insights into sharper underwriting and pricing strategies.
- Analyse loan performance and portfolio trends (approval rates, PD/expected loss, vintage curves, risk-adjusted yield, and profitability)



to identify emerging risks and recommend policy changes.
- Leverage AI/ML tools to extract structured signals from unstructured cash flow, bank statement, and alternative data at scale, accelerating insight generation for underwriting and pricing decisions.
- Partner with Data Science and Data Engineering on model development and analysing model outputs, integrating the models in the underwriting, analysing alternative data, bureau attributes, and on-book behavioural data attributes.
- Run champion/challenger tests and A/B experiments on underwriting cutoffs, pricing, and refinance offers, and build the business case for scaling winning strategies.
- Present underwriting, pricing, and volume recommendations to the Risk Management Committee, senior leadership, and cross-functional stakeholders.
- Mentor junior risk analysts and contribute to best practices in strategy development, experimentation, and portfolio analytics.

Required Qualifications • Bachelor's degree in a quantitative field (Statistics, Computer Science, Mathematics, Economics, Engineering, or related); advanced degree a plus.

- 3-6 years of experience in data science, analytics, credit risk, or risk strategy, preferably in lending, fintech, banking, or other risk-driven industries.
- Strong SQL skills and ability to independently analyse large datasets and portfolio performance; working knowledge of AI/ML tools to accelerate data mining and insight generation.
- Strong knowledge of Python/R
- Strong quantitative and analytical foundation, with the ability to interpret model outputs and translate analysis into actionable credit strategies.
- Strong understanding of the trade-offs between growth, credit risk, pricing, and profitability.
- Excellent communication and stakeholder-management skills - able to build the business case for policy and pricing changes with Risk Committees, senior leadership, and cross-functional teams.

📌 Senior Data Scientist – Credit Risk (India)
🏢 Applied Data Finance
📍 India