Data Science Advisor

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 Internal Audit team, you will serve as a coach, mentor, and subject matter expert, advancing products and initiatives through insights, recommendations, process improvement, automation, and predictive modeling. You will apply expertise in data science, machine learning, AI, large-scale data processing, computational programming, and practical problem solving, while clearly explaining technical solutions to non-technical partners and stakeholders. As an advisor, you will partner across Audit, Technology, Enterprise AI, data science, and risk to architect reusable products on a unified platform that delivers AI-enabled capabilities for stronger risk detection, continuous monitoring, evidence generation, and control-risk reporting. You will also help shape the organization's strategy for applying AI and data science to deliver 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.

• 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.

• 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.

• 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.