• Develop and maintain scalable, resilient data pipelines (ETL/ELT). • Design and implement data architectures in cloud and/or on-premises environments. • Integrate data via APIs, web services, and automated processes. • Build and maintain data collection solutions (crawlers, scrapers, batch and streaming ingestion). • Ensure data quality, standardization, and governance. • Monitor and optimize the performance of data pipelines and data processes. • Support business and technology teams with data-driven solutions. • Document processes, workflows, and architectures following best practices. • Contribute to the continuous evolution of data solutions and tools.

• Lead the design, development, and implementation of advanced machine learning models and algorithms to solve complex business problems. • Collaborate with cross-functional teams, including product managers, engineers, and business stakeholders, to identify data-driven opportunities and translate them into actionable insights. • Analyze large datasets to extract meaningful patterns, trends, and relationships, leveraging statistical methods, machine learning techniques, and data visualization tools. • Develop and validate approaches, ensuring accuracy, robustness, and scalability for deployment in production environments. • Communicate findings and recommendations to both technical and non-technical audiences through detailed reports, presentations, and data visualizations. • Mentor and provide guidance to junior data scientists and analysts, fostering a culture of continuous learning and innovation.

• Lead portfolio performance and risk-trend analyses, incorporating macroeconomic context and translating results into business recommendations. • Provide insights beyond core datasets by triangulating quantitative findings with customer behavior, operational signals, and market dynamics. • Lead risk-based pricing strategy and model development to optimize expected loss, yield, and unit economics across acquisitions and originations. • Monitor pricing performance and drive tests/guardrails to improve outcomes. • Set strategy and KPIs to improve funder, underwriting, and servicing effectiveness across key credit dimensions. • Drive initiatives to reduce delinquency, losses, and roll-rates with prioritized action plans. • Design and measure collection and servicing strategies, standardize reporting and data quality monitoring. • Lead a team of data scientists and deliver scalable, AI-ready datasets. • Partner with Risk on underwriting and pricing, monitoring model performance, and driving improvements. • Partner with Sales to provide analytics support for program enhancements.

• The Senior Associate, Data Analytics role will work closely with the manager and department head of analytics to support the company’s growth through high-impact analyses across internal and external data sources. • As part of a small Advance Analytics & Enterprise Data team, you will own analytics workstreams end-to-end—from framing the question and building the dataset to delivering clear, executive-ready insights that inform decision-making, surface emerging risks and opportunities, and improve performance. • Build and maintain analytical infrastructure to monitor portfolio performance, including KPI dashboards, recurring reporting, and self-serve tools. • Derive analytical insights from portfolio data and evaluate risk profile trends. • Provide insights beyond the data by connecting quantitative findings with business context, customer behavior, and market dynamics; identify trends and leading indicators not directly visible in core datasets. • Develop pricing models to evaluate and price bulk portfolios for purchase. • Conduct analysis on portfolio impact due to changes in macroeconomic factors and make business recommendations. • Develop metrics to monitor and improve funder and underwriter effectiveness along several key credit dimensions. • Formulate actionable tasks to lower delinquency rates, losses and roll-rates. • Ideate and innovate superior collection strategies to increase the effectiveness and efficiency of collector activities. • Identify and support operational process improvements by leveraging technology and analytical tools; help standardize and automate report generation and data quality monitoring. • Partner with Servicing Operations team to leverage analytics to develop superior operational processes. • Partner with Technology team for evaluation and roll out of cutting-edge innovation in financial technology. • Coordinate with Risk team to refine existing underwriting models and build newer risk models. • Support the manager and director with ad hoc analyses to address specific business problems; synthesize findings into clear, executive-ready narratives and recommended actions. • Partner with Data Engineering team to build AI ready dataset.