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Data Scientist Advisor (Flexible Hybrid)

Company Description

At Fannie Mae, futures are made. The inspiring work we do helps make a home a possibility for millions of homeowners and renters. Every day offers compelling opportunities to impact the future of the housing industry while being part of an inclusive team thriving in an energizing, flexible environment. Here, you will grow your career and help create access to fair, affordable housing finance.

Job Description

As a valued contributor to our team, you will act as a coach, mentor, and subject matter expert to drive the success of portions of products or initiatives through the production of insights, new product or change recommendations, process improvement or automation, and predictive modeling. You will apply extensive knowledge of data mining and data analysis methods, be adept with common large data processing techniques, computational programing capabilities, practical problem-solving skills, and have an expert ability to articulate solutions to non-technical consumers or partners. As an advisor, you will partner with data engineering and data management teams, and apply data mining techniques to external or created data sources in preparation for analysis or use of enterprise data assets.

THE IMPACT YOU WILL MAKE
The Data Scientist 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:

  • Collaborate with product and/or business owners, data engineers, and platform teams to understand business needs and current capabilities, data availability, and alternative uses.
  • Design advanced modeling and machine learning applications to support credit risk management, stress testing, property valuations, and business performance for parts of products or initiatives.
  • Apply insights from analysis and market trends to develop assumptions and scenarios to perform credit risk and regulatory capital forecasting and stress testing, key assumption sensitivities, and other analytics as necessary.
  • Manage monthly risk reporting and provide insightful commentaries that highlight existing and emerging risk trends to help senior management make effective strategic decisions
  • Design data visualizations, technical documentation, and non-technical presentation materials to communicate new ideas and high-impact solutions to business partners.

Qualifications

THE EXPERIENCE YOU BRING TO THE TEAM

Minimum Required Experience

  • Advanced Degree in Data Science, Economics, Math, Statistics, or a related field
  • 6 years of research or industry experience in quantitative finance, economics, credit risk modeling or big data analytics using R, Python or SQL
  • Relevant exposure to real estate valuation approaches for both single-family and multifamily properties.

Desired Experiences

  • Proven track record of successfully designing and delivering statistical, data science and analytics projects that have strategic business impact with scalability requirements
  • Experience with advanced statistical sampling methods, A/B testing, Montecarlo simulation and variance reduction techniques
  • Familiarity with corporate finance and CECL accounting, Basel III, Regulatory Capital Requirements and DFAST.
  • Strong business acumen with the ability to digest complex issues and communicate in a concise manner to influence desired outcomes.
  • Cross-functional business skills with excellent facilitation and communication skills, strong influencing skills, and ability to effectively present to and work with senior management
  • Experience mentoring and leading junior analysts and data engineers
  • Adept at managing multiple projects through effective planning, developing product roadmaps, delegating and managing people and timelines to ensure successful project completion.
  • Skilled in AWS Machine Learning tools such as SageMaker, Lex, Polly, or Forecast
  • Experience using Tableau, MicroStrategy and other data visualization tools

Additional Information

REF13445X

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

Fannie Mae is a flexible hybrid company. We embrace flexibility for our employees to work where they choose, while also providing office space for in-person work if desired. At times, business need may call for on-site collaboration, which means proximity within a reasonable commute to your designated office location is preferred unless job is noted as open to remote.


Fannie Mae is an Equal Opportunity Employer, which means we are committed to fostering a diverse and inclusive workplace. All qualified applicants will receive consideration for employment without regard to race, religion, national origin, gender, gender identity, sexual orientation, personal appearance, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation in the application process, email us at [email protected].

The hiring range for this role is set forth on each of our job postings located on Fannie Mae's Career Site. 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.

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What You Should Know About Data Scientist Advisor (Flexible Hybrid), Fannie Mae

Join Fannie Mae as a Data Scientist Advisor in Washington, DC, and become an integral part of a team that makes home ownership a reality for millions! This role is perfect for someone who loves to mentor, coach, and drive innovation through powerful insights. You'll work closely with product owners and data engineers, turning complex data into strategic solutions that significantly impact our business and the housing industry. Your expertise in data mining and advanced modeling will shine as you create machine learning applications, optimize credit risk management, and deliver insightful reports. Not only will you analyze market trends to support risk forecasting, but you'll also get the chance to create compelling data visualizations and presentations for our business partners. With a flexible hybrid work environment at Fannie Mae, you’re encouraged to design a work schedule that suits you best while thriving within a collaborative team culture. If you're eager to make a difference and shape the future of housing finance, this is the role for you!

Frequently Asked Questions (FAQs) for Data Scientist Advisor (Flexible Hybrid) Role at Fannie Mae
What are the key responsibilities of a Data Scientist Advisor at Fannie Mae?

As a Data Scientist Advisor at Fannie Mae, your primary responsibilities include collaborating with various teams to understand business needs, designing advanced modeling and machine learning applications, and analyzing market trends. You'll manage risk reporting while providing insights to help senior management make informed decisions. Furthermore, you will create technical documentation and communicate innovative ideas effectively to business partners.

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What qualifications do I need to become a Data Scientist Advisor at Fannie Mae?

To qualify for the Data Scientist Advisor role at Fannie Mae, candidates should hold an advanced degree in fields such as Data Science, Economics, Math, or Statistics. Additionally, six years of experience in quantitative finance or analytics, particularly in credit risk modeling using tools like R, Python, or SQL, is required. Familiarity with real estate valuation, advanced statistical methods, and corporate finance will set you apart as a strong candidate.

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What tools and skills are essential for the Data Scientist Advisor position at Fannie Mae?

Essential tools and skills for the Data Scientist Advisor role include proficiency in programming languages like R, Python, SQL, and experience with AWS Machine Learning tools such as SageMaker. Familiarity with data visualization software like Tableau or MicroStrategy, as well as strong communication and strategic thinking abilities, are also important for successfully conveying complex analyses to diverse stakeholders.

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How does the Data Scientist Advisor role at Fannie Mae contribute to the company's success?

The Data Scientist Advisor role at Fannie Mae is pivotal to the company's success as it enables data-driven decision-making that directly impacts the housing finance industry. By applying advanced analytics and predictive modeling, you will help identify risks, improve product initiatives, and enhance overall performance, ensuring Fannie Mae maintains a strong market position.

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What is the work culture like for a Data Scientist Advisor at Fannie Mae?

The work culture for a Data Scientist Advisor at Fannie Mae is inclusive, vibrant, and flexible. Fannie Mae embraces hybrid work, allowing team members to balance remote and in-office work, fostering collaboration and innovation. The company's commitment to diversity creates a positive and enriching environment where employees can thrive and grow their careers.

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Common Interview Questions for Data Scientist Advisor (Flexible Hybrid)
Can you explain your experience with credit risk modeling?

When addressing your experience with credit risk modeling, provide specific examples of projects you've worked on, the methodologies you applied, and the outcomes achieved. Discuss your approach to risk assessment, the tools used, and how your contributions led to strategic business improvements.

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How do you approach data visualization for non-technical stakeholders?

To effectively approach data visualization for non-technical stakeholders, focus on clarity and simplicity. Explain your thought process on choosing specific charts and graphics that convey the data's story while avoiding jargon. Share an example that highlights how your visualizations led to actionable insights.

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What is your experience with machine learning applications?

When discussing your experience with machine learning applications, detail the types of machine learning models you've developed, the datasets used, and the impacts of these models on projects. Include any challenges faced, how you overcame them, and any innovative applications you've implemented.

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Describe a situation where you had to mentor a junior analyst.

In discussing your mentorship experience, share a specific example where you guided a junior analyst through a project. Highlight your approach, the skills you focused on developing, and the positive outcomes that resulted from your mentorship.

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How do you prioritize multiple projects as a Data Scientist Advisor?

Explain your prioritization strategy, emphasizing the importance of aligning projects with the company's goals. Discuss tools or methodologies you use for project management and how you keep stakeholders informed about progress and challenges.

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What statistical methods do you favor when analyzing large datasets?

Share the statistical methods you find most effective for analyzing large datasets, such as regression analysis, A/B testing, or Monte Carlo simulations. Discuss why these methods are beneficial and provide examples of projects where you've successfully applied them.

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How do you stay updated with industry trends in data science and finance?

Discuss various resources you utilize to stay informed about data science and finance, such as online courses, webinars, industry publications, and professional networks. Share how these resources have positively influenced your work and kept your skills relevant.

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Can you walk us through your data analysis process?

Provide a detailed walkthrough of your data analysis process, from problem definition to data collection, analysis, and presentation. Emphasize how you ensure accuracy and clarity at each stage and share a specific project example demonstrating this process.

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How do you communicate complex data insights to business partners?

Describe your techniques for simplifying complex data insights for non-technical audiences. Discuss the importance of tailoring your message based on your audience's background and using visuals to aid understanding.

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What projects have you completed that demonstrate your strategic impact?

When discussing projects showcasing your strategic impact, describe the context, your specific role, and the methodologies used. Highlight the results and lessons learned, emphasizing how your work influenced business strategies.

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Fannie Mae’s mission is to facilitate equitable and sustainable access to homeownership and quality, affordable rental housing across America.

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Full-time, hybrid
DATE POSTED
January 9, 2025

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