Data Scientist-Collections Analytics

As a Data Scientist – Collections Analytics, support the development and optimization of strategies for payment recovery by analyzing data, monitoring performance, ensuring compliance, and identifying opportunities to minimize losses and improve cash flow. The role requires strong analytical and communication skills, with experience or exposure to Collections or Credit Risk environments preferred. Key duties include tracking KPIs and collaborating with departments such as Finance and Credit to align collection efforts with business goals.

Responsibilities

• Support the development, implementation, and tracking of existing and new strategies to optimize collections efforts.

• Design tailored treatment strategies: Assist in creating differentiated collection strategies and communication flows for each segment within the ATP/WTP matrix.

• Track and report on key performance indicators (KPIs) and recovery rates for each matrix segment, providing insights and recommending corrective actions.

• Utilize data analytics to determine the most effective timing and frequency for Automated Clearing House (ACH) payment retries to maximize successful payment captures while minimizing customer fees, banking issues, and potential impact on the customer relationship.

• Continuously research and pilot new collection opportunities, such as leveraging alternative data sources for improved risk assessment.

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

• Partner with product development and engineering teams to support the automation and implementation of new strategies and assistance programs within existing platforms, ensuring an efficient customer experience.

Qualifications

Bachelor of Engineering or Masters degree in Quantitative disciplines (Statistics, Mathematics, Engineering, Economics, Data Science, or related fields).

Experience

1–3 years of experience in Data Science or Analytics roles.

Prior experience or exposure to 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 learning applications 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 analytics environments.

Soft Skills

Strong analytical and problem-solving abilities.

Excellent communication and stakeholder management skills.

Ability to work independently and collaborate with cross-functional teams.