Commercial Loans Senior Data Analyst

SoFi•Sacramento, CA

About The Position

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.

Requirements

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.

Nice To Haves

Advanced degree (MS/MBA/PhD) in a quantitative field.
Familiarity with modern data stacks (e.g., Snowflake/BigQuery/Redshift, dbt, Airflow) and version control (Git).
Experience supporting capital markets needs: investor reporting, securitization support, due diligence, credit tape analysis, and guideline overlays.
Experience designing and evaluating policy tests/experiments (A/B testing where applicable, quasi-experimental measurement, monitoring for unintended impacts).
Tools: JIRA/Confluence, Airtable, and project planning practices for analytics delivery.

Responsibilities

Own portfolio performance analytics (core):
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.
Credit risk, concentration, and early warning monitoring:
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.
Credit strategy & policy feedback loop:
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.
Build a closed-loop system:
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).
Cross-functional delivery & execution:
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.