Hiring: Data Scientist (Collections Analytics) at Applied Data Finance – Remote/India

Data Scientist (Collections Analytics) at Applied Data Finance (ADF)

Applied Data Finance (ADF) is a leading US-based fintech that leverages advanced data science to provide credit to underbanked consumers. This Collections Analytics role is a high-impact position focused on the “recovery” side of the lending lifecycle—using data to ensure the business remains profitable while maintaining positive customer relationships.

🟢 About the Company: Applied Data Finance

ADF is known for its Personify Financial brand. They operate as a tech-first lender, meaning their collection strategies aren’t just about making phone calls; they are driven by sophisticated ATP/WTP matrices (Ability to Pay / Willingness to Pay) and machine learning models to determine the best way to interact with each individual customer.

About the Role: Data Scientist – Collections Analytics

In this role, you will be the architect of recovery. You will analyse borrower behaviour to determine who needs a gentle reminder, who needs a structured payment plan, and when is the mathematically “perfect” time to retry a failed payment.

Core Responsibilities

• Strategy Optimisation: Develop and track differentiated collection strategies based on the ATP/WTP matrix(Segmenting customers by their financial capacity vs. their intent to pay).

• Payment Capture Logic: Use data to optimise ACH retry logic—calculating the exact timing and frequency to maximise successful payments while avoiding excessive NSF (Non-Sufficient Funds) fees for the customer.

• KPI Management: Track recovery rates and loss mitigation metrics, providing clear data storytelling to the Finance and Credit departments.

• Pilot Programs: Research and test alternative data sources (e.g., utility payments, behavioural data) to improve risk and recovery assessments.

• Automation: Collaborate with Engineering to turn your analytical insights into automated “treatment flows” within the lending platform.

   

Key Qualifications & Technical Stack

• Experience: 1–3 years in Data Science. Prior exposure to Credit Risk, Collections, or Fintech is a major advantage.

• Technical: Mastery of Python and SQL is mandatory. You must be comfortable working with large, messy transactional datasets.

• Education: Degree in a quantitative field (Engineering, Stats, Math, Economics, or Data Science).

📊 Compensation & Salary Benchmarks (2026)

Based on verified 2026 data for Applied Data Finance in India: