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Credit Risk Data Science Jobs in Washington, DC (NOW HIRING)

As an advisor, you will partner across Audit, Technology, Enterprise AI, data science, and risk organizations to architect reusable products on a unified platform that delivers AI-enabled ...

As an advisor, you will partner across Audit, Technology, Enterprise AI, data science, and risk organizations to architect reusable products on a unified platform that delivers AI-enabled ...

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Credit Risk Data Science information

See Washington, DC salary details

$41.9K

$129K

$223.7K

How much do credit risk data science jobs pay per year?

As of Jul 7, 2026, the average yearly pay for credit risk data science in Washington, DC is $128,981.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,400.00 and $159,100.00 per year, depending on experience, location, and employer.

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 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.
What job categories do people searching Credit Risk Data Science jobs in Washington, DC look for? The top searched job categories for Credit Risk Data Science jobs in Washington, DC are:

Audit Data Science Advisor

Fanniemae

Washington, DC • On-site

Full-time

Medical, Life

Posted 11 days ago


Job description

Playing an essential role in the U.S. economy, Fannie Mae is foundational to housing finance. Here, your expertise can help fuel purpose-driven innovation that expands access to homeownership and affordable rental housing across the country. Join Fannie Mae to grow your career and help people find a place to call home.

Job Description

As a valued contributor to our team, you will serve as a coach, mentor, and subject matter expert, driving the success of product and initiative workstreams through insights, product and change recommendations, process improvement, automation, and predictive modeling. You will apply deep expertise in data science, machine learning, AI techniques, large-scale data processing, computational programming, and practical problem solving, with the ability to clearly explain technical solutions to non-technical partners and stakeholders. As an advisor, you will partner across Audit, Technology, Enterprise AI, data science, and risk organizations to architect reusable products on a unified platform that delivers AI-enabled capabilities for stronger risk detection, continuous monitoring, evidence generation, and control-risk insights reporting. In addition, you will help shape the organization's strategy for using and developing AI and data science to deliver data-driven insights and sound business judgment. In addition, you will provide expert guidance on well-governed models and analytical tools, partnering with senior leadership to advance business and AI transformation and innovation.

THE IMPACT YOU WILL MAKE

The Audit Data Science Advisor role will offer you the flexibility to make each day your own, while working alongside people who care so that you can deliver on the following responsibilities:

  • Partner across Audit, Technology, and platform teams to build a unified Audit platform with reusable data, analytics, automation, GenAI services, model operations, secure delivery, and enterprise controls.

  • Develop advanced analytics, AI, and data science solutions to solve complex business and technical challenges and shape technical direction.

  • Design, test, and validate audit solutions using advanced data science methods aligned with audit standards and methodology.

  • Apply data science to improve risk measurement, valuation, decision-making, and business performance.

  • Create technical strategies and executive-ready materials that communicate high-impact solutions to leaders and stakeholders.

  • Provide thought leadership on applying advanced analytics and data science to business challenges.

  • Build solutions for continuous monitoring, risk detection, automated evidence generation, and deeper insights.

  • Assess model effectiveness and fitness for use, ensure testing and monitoring, and explain key drivers and limitations.

  • Lead cross-functional teams through the model lifecycle, aligning changes with business goals.

  • Stay current on industry practices, regulations, and internal standards to ensure compliance and escalate issues as needed.

  • Advise senior leaders on priorities, balancing accuracy, speed, cost, and governance.

  • Support the Model Owner and Lead Model User in building consensus, prioritizing requirements, testing changes, resolving findings, and sharing best practices.

  • Drive continuous improvement in modeling and analytics while promoting accountability, transparency, and proactive model risk management.

  • Represent the Analytics team in internal forums, regulatory settings, and industry conferences, sharing best practices and thought leadership.

THE EXPERIENCE YOU BRING TO THE TEAM

Minimum Required Experiences

  • 6 years of related experience in data science, machine learning, and AI solution development, including GenAI workflows.

  • Master's degree in Data Science, Economics, Mathematics, Statistics, Computer Science, or a related field.

  • Advanced proficiency in Python and core data science and machine learning techniques.

  • Experience building end-to-end data science solutions using AWS data services such as Redshift, Athena, S3, and AWS Data Wrangler.

  • Strong analytical skills to support testing, validation, model assessment, and business decision-making.

  • Strong communication skills, including the ability to explain technical concepts and solutions to non-technical partners and stakeholders.

  • Shows curiosity and adaptability in learning and responsibly applying new technologies, including artificial intelligence, to reimagine how we work.

Desired Experiences

  • PhD in Data Science, Economics, Mathematics, Statistics, Computer Science, or a related field, or equivalent additional experience.

  • Experience in Internal Audit, Risk Management, Model Risk Management, or other highly regulated environments.

  • Experience applying advanced data science methods such as regression, SVM (support vector machines), random forests, and neural networks.

  • Strong technical writing, presentation, and executive stakeholder communication skills.

  • Proven ability to influence and collaborate across cross-functional teams, including Audit, Technology, AI, and Risk partners.

  • Experience with AI engineering tools and patterns, such as Anthropic or OpenAI models and tool-calling frameworks.

Internal Audit - Data Science - Advisor

#LI-Hybrid

Qualifications

Education:

Bachelor's Level Degree

The future is what you make it to be. Discover compelling opportunities at Fanniemae.com/careers.

For most roles, employees are expected to work onsite on a regular basis at their designated office location. In-office work cadence is determined by your manager. Proximity within a reasonable commute to your designated office location is preferred unless the job is noted as open to remote.


Fannie Mae is an equal opportunity employer and considers qualified applicants for employment without regard to race, color, religion, sex, national origin, disability, age, sexual orientation, gender identity/gender expression, marital or parental status, or any other protected factor. Fannie Mae is committed to providing reasonable accommodations to qualified individuals with disabilities who are employees or applicants for employment, unless to do so would cause undue hardship to the company. If you need assistance using our online system and/or you need a reasonable accommodation related to the hiring/application process, please complete this form.

The hiring range for this role is set forth below. Final salaries will generally vary within that range based on factors that include but are not limited to, skill set, depth of experience, certifications, and other relevant qualifications. This position is eligible to participate in a Fannie Mae incentive program (subject to the terms of the program). As part of our comprehensive benefits package, Fannie Mae offers a broad range of Health, Life, Voluntary Lifestyle, and other benefits and perks that enhance an employee's physical, mental, emotional, and financial well-being. See more here.

Requisition compensation:

155000

to

209000