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Credit Risk Data Science Jobs in Virginia (NOW HIRING)

... data-driven, and compliant with evolving regulatory expectations. The Manager will partner closely with First Line business teams while maintaining independence, contribute to executive and committee ...

The Credit Manager for Single-Family Seller Credit Risk Management is tasked with identifying ... Analytical and data-driven approach to risk assessment, along with interest in utilizing AI tools ...

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Credit Risk Data Science information

What are the key skills and qualifications needed to thrive as a Credit Risk Data Scientist, and why are they important?

To thrive as a Credit Risk Data Scientist, you need strong analytical skills, proficiency in statistical modeling, and a solid background in finance, mathematics, or a related field, often supported by an advanced degree. Familiarity with programming languages like Python or R, experience with machine learning frameworks, and knowledge of credit risk modeling tools such as SAS or SQL are typically required. Critical thinking, attention to detail, and effective communication are vital soft skills for interpreting data and collaborating with stakeholders. These abilities are crucial for building accurate risk models, informing strategic decisions, and ensuring regulatory compliance in financial institutions.

How does a Credit Risk Data Scientist typically collaborate with other teams within a financial institution?

Credit Risk Data Scientists often work closely with credit analysts, risk managers, and IT professionals to develop, validate, and implement models that assess borrower risk. They frequently participate in cross-functional meetings to translate complex analytical findings into actionable business insights. Collaboration with compliance and regulatory teams is also common to ensure that risk models meet current regulatory standards. Effective communication and teamwork are essential, as the role bridges technical model development and practical risk management decisions.

What is Credit Risk Data Science?

Credit Risk Data Science is a specialized field that uses statistical analysis, machine learning, and data modeling techniques to assess and predict the likelihood that a borrower will default on a loan or credit obligation. Professionals in this field analyze large datasets from financial transactions, credit reports, and market trends to develop models that help financial institutions make informed lending decisions. Their work helps manage risk, set appropriate interest rates, and comply with regulatory standards. By leveraging advanced analytics, credit risk data scientists play a crucial role in minimizing losses and maximizing profitability for banks and lenders.
What job categories do people searching Credit Risk Data Science jobs in Virginia look for? The top searched job categories for Credit Risk Data Science jobs in Virginia are:
What cities in Virginia are hiring for Credit Risk Data Science jobs? Cities in Virginia with the most Credit Risk Data Science job openings:
Sr. Director of Credit Risk Analytics

Sr. Director of Credit Risk Analytics

Koalafi

Richmond, VA

Other

Posted 4 days ago


Job description

What You'll Do 

The Sr. Director of Credit Risk Analytics is a senior leadership role responsible for the full Credit Policy function - from strategy design through execution and measurement. You will own the credit program's analytical infrastructure, set the team's agenda, and be the primary accountable owner for portfolio profitability. This is a high-visibility role that requires equal parts strategic vision and quantitative rigor, with a strong bias toward structured experimentation and evidence-based decision-making. 

You will report directly to the Chief Risk Officer and serve as a key cross-functional partner to the Data Science, Finance, Product/Tech, and Sales teams. 

Credit Policy Strategy & Program Ownership 

  • Define and own the Credit Policy roadmap, setting the team's agenda in alignment with company growth, risk appetite, and profitability objectives
  • Determine which underwriting strategies, credit policy levers, and product term configurations to pursue - and in what sequence
  • Partner with the Data Science team to translate policy intent into model inputs, scorecard strategies, and decisioning logic
  • Ensure that credit policy decisions account for second-order effects, including merchant engagement and performance impacts associated with changes 

Experimentation & Strategy Measurement 

  • Design, execute, and monitor controlled experiments (A/B tests, champion/challenger frameworks) for all material credit policy and product term changes prior to full rollout
  • Establish rigorous experimental design standards that ensure clean attribution - isolating the impact of individual policy changes from macro, seasonal, and cohort effects
  • Define the measurement framework for each test, identifying the appropriate primary and guardrail metrics
  • Synthesize test results into clear, defensible recommendations and present findings to senior leadership and cross-functional partners 

Portfolio Performance Monitoring & Analytics 

  • Own ongoing monitoring of portfolio health across a comprehensive suite of KPIs, including delinquency rates, roll rates, early buyout rates, loss curves, and terminal outcomes by cohort, vintage, vertical, and channel
  • Build and maintain analytical frameworks that allow the team to quickly surface emerging portfolio trends and distinguish signal from noise
  • Identify risk concentrations, underperforming segments, and policy gaps, and drive timely remediation through credit policy adjustments 

Portfolio Forecasting 

  • Lead portfolio forecasting using a combination of horizontal and vertical performance metrics to produce reliable point-in-time portfolio performance projections
  • Partner with FP&A and Capital Markets to ensure credit forecasts are integrated into financial planning, capital allocation, and investor reporting processes
  • Develop scenario analysis and stress-testing capabilities to assess the portfolio's sensitivity to macroeconomic changes, product mix shifts, and channel growth assumptions 

Valuations Framework & Unit Economics 

  • Design and own a valuations framework and model that enables analysts on the team to rapidly and reliably model the unit economics impact of changes to credit policy, product terms, and origination mix
  • Ensure the model is flexible enough to support "what-if" scenario gaming across key levers (e.g., APR equivalents, payment cadence, term length, approval tier thresholds) while maintaining analytical integrity
  • Work with Finance and Capital Markets to align valuation assumptions with capital markets pricing, funding cost structures, and investor reporting standards 

Team Leadership & Development 

  • Lead, mentor, and develop a team of four: a Senior Analyst, Manager, Senior Manager, and Director - establishing clear goals, growth paths, and accountability structures
  • Foster a culture of intellectual rigor, curiosity, and ownership within the team
  • Build scalable analytical processes and documentation standards that reduce key-person dependency and support team growth as the business scales
  • Represent the Credit Policy function in cross-functional forums and serve as a thought partner to peer leaders across Risk, Data Science, Product, and Finance 

About You (Qualifications) 

  • Bachelor's degree required, in a quantitative field such as Statistics, Mathematics, Economics, Business, or Engineering preferred
  • 7+ years of experience in an analyst, data science, or consulting role
  • 5+ years of experience in credit risk analysis, credit policy, or a closely related function within consumer or small business lending, leasing, or installment finance
  • Prior experience with Lease-to-Own, BNPL, installment lending, other non-traditional consumer credit products preferred 
  • Deep proficiency in credit performance analytics: delinquency curves, roll rate analysis, loss forecasting, vintage analysis, and cohort-based performance attribution
  • Expert-level command of experimental design, including A/B testing, champion/challenger frameworks, and statistical significance testing; a demonstrated ability to isolate causal effects and defend attribution in complex, multi-variable environments
  • Strong financial modeling skills including the ability to translate credit performance assumptions into unit economics, portfolio valuations, and P&L impact
  • Proficiency in Excel, SQL, and Python; comfort working with large datasets and interfacing with data science and data engineering teams
  • Proven ability to lead and develop analytical teams, including managing managers
  • Exceptional communication skills - able to translate complex quantitative findings into crisp, actionable narratives for executive and cross-functional audiences
  • High degree of intellectual honesty; comfortable challenging assumptions, acknowledging uncertainty, and changing course when data warrants it
  • Strong instincts for prioritization in a fast-moving, resource-constrained environment 
  • Location Requirement: This position requires regular in-person attendance at one of our two office locations (Richmond, VA or Arlington, VA). Candidates must already be located within a commutable distance to either location, as relocation assistance is not available at this time.