Job Title : Data Scientist Collections Analytics

As a Data Scientist-Collections Analytics, 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 optimize collections efforts.
Design Tailored Treatment Strategies : Create specific, 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 detailed insights and recommending corrective actions.
Utilize data analytics to determine the most effective timing and frequency for automated clearing house (ACH) payment retries. The goal is to maximize successful payment captures while minimizing customer fees, banking issues, and potential damage to the customer relationship.
Continuously research and pilot new collection opportunities, such as leveraging alternative data sources for better risk assessment.
Distill complex data analysis and strategic initiatives into clear, compelling presentations and effectively communicate key insights, performance results.
Partner with product development and engineering teams to automate the delivery of these new strategies and assistance programs within existing platforms, ensuring seamless and efficient customer experience.

Qualifications

Bachelor of Engineering or Masters degree in Quantitative disciplines (Statistics, Mathematics, Engineering, Economics, Data Science, or related fields).
Experience 1 to 3 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 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 production-grade analytics environments.

Soft Skills

Strong analytical and problem-solving abilities.
Excellent communication and stakeholder management skills.
Ability to work independently and collaboratively in cross-functional teams.

(ref:hirist.tech)

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