Senior Data Analyst, SoFi

• Strong technical data skills: advanced SQL (complex joins, window functions, performance optimization), and proficiency in Python (pandas, numpy, data manipulation, statistical analysis). Experience building reproducible analysis and scalable datasets
• Data modeling & analytics engineering: ability to work with large datasets, design clean data structures, and partner with Data Engineering on pipelines (e.g., dbt, Airflow)
• 3 years of Commercial lending analytics expertise: portfolio segmentation, credit performance analysis, and lifecycle modeling
• Decision science mindset: hypothesis-driven analysis, experiment design, and quantifying trade-offs (risk vs. growth vs. profitability)
• Data rigor: strong discipline around metric definitions, reconciliation, QA, and documentation to ensure consistent reporting
• Business translation: ability to convert complex analysis into clear, actionable recommendations for credit strategy, underwriting, and product decisions
• Cross-functional influence: ability to partner effectively with Credit, Risk, Finance, Product, and Engineering to drive change
• Execution excellence: delivering high-quality dashboards, datasets, and monitoring tools, with a focus on scalability and continuous improvement

Desirable

• We are seeking an experienced, technically strong Senior Data Analyst – Commercial Lending to own end-to-end analytics for our commercial lending portfolio and translate performance insights into a measurable feedback loop that improves credit strategy, risk management, and profitability
• In this role, you will analyze portfolio performance across the full lifecycle (origination → underwriting → servicing → repayment/default outcomes), build scalable reporting and monitoring, and partner closely with Credit, Underwriting, Risk, Operations, Finance, Compliance, and Product to turn data into actionable decisions
• You will define the “source of truth” for portfolio performance, identify emerging risks and growth opportunities, and drive data-informed changes to credit policy, underwriting strategies, pricing, and operational processes—while measuring their impact
• Define and maintain KPIs across credit risk, growth, and profitability (e.g., delinquency rates, default rates, loss rates, recovery rates, utilization, yield, risk-adjusted return metrics)
• Perform cohort/vintage and segmentation analysis across borrower attributes (industry, revenue, risk rating, collateral type, geography, loan size, term, product type)
• Build and maintain executive dashboards and automated reporting that clearly explain performance trends, drivers, and recommended actions
• Partner with Data Engineering to ensure scalable data models, clean metric definitions, and reliable reporting pipelines
• Monitor portfolio concentrations across industries, geographies, borrower segments, and product types
• Develop and track early warning indicators (e.g., payment behavior changes, utilization shifts, covenant signals, sector stress)
• Assess impact of macroeconomic and market conditions (rates, sector performance, liquidity trends) on portfolio risk and returns
• Analyze credit policy performance and underwriting decisions, identifying opportunities to optimize approval rates, loss outcomes, and risk-adjusted returns
• Conduct root-cause analysis on defaults, exceptions, and losses; quantify business impact
• Identify key drivers of performance
• Recommend changes (credit policy, pricing, underwriting, limits)
• Partner to implement changes
• Measure impact through structured before/after analysis
• Portfolio optimization & profitability analytics:
• Evaluate risk vs. growth vs. profitability trade-offs across segments and products
• Support pricing strategy and capital allocation decisions through data-driven insights
• Analyze lifecycle economics (origination quality, utilization, retention, loss timing, recovery)
• Partner with Credit, Risk, Product, Finance, and Operations to deliver analytics solutions (dashboards, datasets, monitoring tools)
• Lead analytics initiatives with structured execution (requirements, timelines, stakeholder alignment, QA, rollout)
• Ensure data integrity and governance across all reporting and analytics outputs