Lead Data Scientist – Collections Analytics

Lead Data Scientist Collections Analytics

As a Lead Data Scientist-Collections Analytics (37 years of experience), develop and

implement strategy for payment recovery,analyzing data, ensuring compliance, and

optimizing processes to minimize losses and improve cash flow. Require strong analytical

communication skills, with experience in Collections. Key duties include managing KPIs,

and collaborating across departments like Finance and Credit to align collection efforts

with business goals

Responsibilities

Develop, implement and track, existing and new strategies to optimizecollections efforts.

Design Tailored Treatment Strategies: Create specific, differentiatedcollection strategies and communication flows for each segment within theATP/WTP matrix.

Track and report on key performance indicators (KPIs) and recovery rates for

each matrix segment, providing detailed insights and recommendingcorrective actions

Utilize data analytics to determine the most effective timing and frequency forautomated clearing house (ACH) payment retries. The goal is to maximizesuccessful payment captures while minimizing customer fees, bankingissues, and potential damage to the customer relationship.

Continuously research and pilot new collection opportunities, such asleveraging alternative data sources for better risk assessment.

Distill complex data analysis and strategic initiatives into clear, compellingpresentations and effectively communicate key insights, performance results.

Partner with product development and engineering teams to automate thedelivery of these new strategies and assistance programs within existingplatforms, ensuring seamless and efficient customer experience.

Train and assist junior analysts in the team

.

Qualifications

Bachelor of Engineering or Masters degree in Quantitative disciplines (Statistics,

Mathematics, Engineering, Economics, Data Science, or related fields).

Experience 37 years of experience in Data Science.

Prior experience in Credit Risk or Collections analytics is strongly preferred.

Exposure to fintech, lending or digital financial services is a plus.

Proven experience in data handling, statistical analysis, and machine learningapplications in real-world business problems.

Technical Skills Proficiency in Python and SQL.

Strong understanding of statistical techniques and machine learning algorithms.Experience working with large datasets and production-grade analyticsenvironments. Soft Skills Strong analytical and problem-solving abilities.

Excellent communication and stakeholder management skills. Ability to workindependently and collaboratively in cross-functional teams.