Data Scientist-Collections Analytics

Role Summary\nAs a Data Scientist – Collections Analytics, you will 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, along with experience or exposure to lending-related use cases such as credit risk, collections, or fraud analytics. You will work closely with cross-functional teams including Finance, Credit, Product, and Engineering to align collections efforts with business goals.\n\nResponsibilities\nSupport the development, implementation, and tracking of existing and new strategies to optimize collections efforts.\nDesign tailored treatment strategies and assist in creating differentiated collection strategies and communication flows for each segment within the ATP/WTP matrix.\nTrack and report on key performance indicators (KPIs) and recovery rates for each matrix segment, providing insights and recommending corrective actions.\nUtilize data analytics to determine the most effective timing and frequency for ACH payment retries to maximize successful payment capture while minimizing customer fees, banking issues, and potential impact on the customer relationship.\nApply advanced data analysis and statistical techniques to evaluate strategy effectiveness and identify opportunities for improvement.\nUse statistical inference, advanced statistical analysis, and A/B testing / experimental design to support strategy development and performance assessment.\nContinuously research and pilot new collection opportunities, such as leveraging alternative data sources for improved risk assessment.\nDistill complex data analysis and strategic initiatives into clear presentations and effectively communicate key insights and performance results to both technical and non-technical stakeholders.\nPartner 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.\n\nEducation\nBachelor of Engineering or Master’s degree in quantitative disciplines such as Statistics, Mathematics, Engineering, Economics, Data Science, or related fields.\n\nExperience\n1–3 years of experience in Data Science.\nCandidates should have experience or exposure to credit risk, collections, fraud analytics, or other lending-related use cases.\nCandidates are expected to bring experience from the credit lending, fintech, or digital financial services domain.\nProven experience in data handling, statistical analysis, and machine learning applications in real-world business problems.\nCandidates must demonstrate an understanding of lending-specific business challenges and how data science techniques can be applied to address them.\n\nTechnical Skills\nExperience using Python and SQL.\nStrong understanding of statistical techniques, statistical inference, and advanced statistical analysis.\nExperience with machine learning algorithms, experimental design, and A/B testing.\nExperience working with large datasets and analytics environments.\n\nSoft Skills\nStrong analytical and problem-solving abilities.\nExcellent communication and stakeholder management skills.\nAbility to communicate technical concepts effectively to both technical and non-technical stakeholders.\nAbility to work independently and collaborate with cross-functional teams.