Home Loans Senior Data Analyst

Who we are:

Shape a brighter financial future with us.

Together with our members, we’re changing the way people think about and interact with personal finance.

We’re a next-generation fintech company using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world.

The role:

We are seeking an experienced, technically strong Home Loans Senior Data Analyst to own end-to-end analytics for our residential mortgage portfolio and translate performance insights into a measurable feedback loop that improves lending strategies. In this role, you will analyze loan performance across the lifecycle (origination → servicing → loss/prepay outcomes), build scalable reporting and monitoring, and partner closely with Underwriting, Credit Policy, Operations, Compliance, Capital Markets, 2LOD and Product to turn data into decisions.

You will be responsible for defining the "truth" of portfolio performance, identifying emerging risk and opportunity signals, and driving changes to guidelines, overlays, operational processes, and product strategy then measuring impact.

Joining SoFi means you’re part of a company reshaping financial services through technology, combining the speed of a startup with the scale and rigor of an established industry leader. This is an opportunity to directly influence Home Loans credit risk strategy within a high-growth, high-impact team.

What you’ll do:

Own portfolio performance analytics (core)

• Define and maintain portfolio performance KPIs across credit, profitability, and risk (e.g., delinquency/roll rates, loss rates, severity, prepayment speeds, cure rates, early payment defaults, repurchase risk, defect rates).
  
  


• Perform cohort/vintage and segmentation analysis by credit score, LTV/CLTV, DTI, lien position, documentation type, occupancy, channel, state/metro, property type, and investor/product.
  
  


• Build and maintain executive dashboards and automated reporting that clearly explain what changed, why it changed, and what to do next.


• Partner with Data Engineering to define data requirements and create new data sources as required AND/OR create summarized tables as needed, ensuring data structures support scalable analysis and consistent reporting.
  
  

Concentration, credit risk, and early warning monitoring

• Monitor and report portfolio concentration limits and emerging risk exposures (credit attributes, geography, product/investor mixes).
  
  


• Develop early warning indicators that proactively identify deteriorating performance (e.g., delinquency migration, geographic stress, valuation drift, policy exception trends).
  
  


• Track portfolio risk related to market shifts (home price changes, rate environment, regional decline signals) and quantify impact to credit outcomes.
  
  

Collateral and valuation analytics

• Order, track, and report refreshed collateral values across the portfolio; analyze LTV migration and identify segments with increasing collateral risk.
  
  


• Partner with servicing/operations to ensure collateral refresh processes are auditable, timely, and decision-relevant.
  
  

Credit policy, investor alignment, and feedback loop to strategy

• Conduct gap analyses across investor guidelines and product types; translate guideline differences into data-backed recommendations for eligibility rules, overlays, and workflow controls.
  
  


• Review loan defects, exceptions, and deficiencies; perform root-cause analysis and quantify performance impact (loss, repurchase, cycle time, fallout).
  
  


• Create a closed-loop learning system:
  
  




• Identify drivers of performance (risk and profitability)
  
  


• Recommend strategy changes (policy, pricing, operational controls, product)
  
  


• Implement with partners
  
  


• Measure results via before/after analysis, monitoring, and clear success metrics
  
  

Cross-functional delivery & technical execution

• Partner with Product, Data Engineering, and business stakeholders to deliver analytics solutions (dashboards, metric definitions, datasets, alerts) with strong documentation and governance.
  
  


• Lead the execution of analytics initiatives using structured project practices (requirements, timelines, stakeholder communication, UAT, rollout).
  
  


• Ensure data integrity: metric definitions, reconciliations, QA checks, and consistent reporting across teams.
  
  

Skills that make someone successful in this role

• Portfolio analytics expertise: segmentation, vintage analysis, trend attribution, and performance driver identification.
  
  


• Decision science mindset: hypothesis-driven analysis, measurement design, and quantifying trade-offs (risk vs. growth vs. profitability).
  
  


• Data rigor: reconciliation discipline, metric governance, documentation, and repeatable processes.
  
  


• Business translation: turning analysis into clear actions for underwriting strategy, policy changes, and operational improvements.
  
  


• Influence without authority: partnering across Underwriting, Ops, Compliance, Capital Markets, and Product to implement change.
  
  


• Execution strength: delivering analytics products (dashboards, datasets, alerts) reliably and improving them over time.
  
  

Preferred qualifications (nice to have)

• 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.