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Fannie Mae

Large Enterprises

Fannie Mae (the Federal National Mortgage Association) is a U.S. government-sponsored enterprise that provides liquidity to the mortgage market by purchasing and guaranteeing home loans. It is one of the largest players in the American housing finance system, helping to make mortgages more accessible and affordable.

2 interview notes · updated Jul 2026

Fannie Mae·Data Scientist·Technical Phone Screen

May 2026
Technical interview for a Data Scientist role at Fannie Mae that ended up being way more frontend/QA-architecture heavy than I expected. Two big parts: a conceptual breakdown of UI vs backend testing across a full test pyramid, then a deep-dive test matrix for a promo code feature. Not your typical data science interview.
  • For a single-page app backed by a BFF that calls a payment API, how do you decide what belongs in UI tests versus backend tests, and which layers do you mock at each level of the test pyramid?
  • How do you prevent flaky tests caused by network instability, timing issues, and async behavior, and how do you detect regressions using test oracles, golden images, or contract tests?
  • Where do security checks like XSS, CSRF, and authorization validation belong in the test layers, and how do you enforce idempotency when retries happen?
  • How do you measure test coverage beyond line or branch coverage, specifically around requirements coverage, risk coverage, and data coverage?
  • Design a test matrix for a new promo code field on a checkout page. The code is validated asynchronously via the BFF, updates the order total, persists the promo, and fires an analytics event. Cover functional, boundary, accessibility, cross-compatibility, resilience, contract, and analytics scenarios.
  • For the promo code feature, how do you test resilience scenarios like offline mode, server timeouts, 429 rate limits, and 5xx errors, and how do you ensure the UI eventually reflects server truth after stale state?

“This one sprawled fast.”

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Fannie Mae·Data Scientist·Technical Phone Screen

May 2026
SQL and pandas heavy technical screen for a Data Scientist role at Fannie Mae. Covered a wide range of topics from basic aggregations all the way to window functions and deduplication, with some LeetCode thrown in for good measure. Felt more like a written exam than a conversation.
  • What do COUNT, SUM, AVG, MIN, and MAX do in SQL, and how do they differ from each other?
  • Describe INNER, LEFT, RIGHT, and FULL OUTER joins and give a real use-case for each.
  • What is the difference between UNION and UNION ALL, and when would you prefer one over the other?
  • What is a window function in SQL and how does it behave differently from a regular aggregation?
  • Compare SQL views to physical tables. What are the advantages and disadvantages of each?
  • How do you hide duplicate rows in a result set without actually deleting them from the table?
  • How would you permanently remove duplicate rows from a database table?
  • Write a SQL query to find the Nth highest salary from an employee table.
  • What are some practical techniques to make SQL queries run faster?
  • In pandas, what is the difference between merge, join, and concat, and when should you use each?

“Straightforward but i overthought COUNT.”

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