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The Interview Process
Data Assessment (Take-Home)
Given a massive, messy dataset of raw bank transaction strings (e.g., 'SQ *CVS PHARM #1234'), you must build a model to clean and categorize them into standard merchants.
Technical Screen
Reviewing your code, discussing your feature engineering choices, and answering advanced statistical questions.
Onsite Loop
Focusing on Applied Machine Learning (how you deploy models to production latencies), A/B testing frameworks, and business intuition.
Real Plaid Interview Questions
Practice these exact questions faced by previous Data Scientist / Machine Learning Engineer candidates.
1Plaid categorizes billions of messy transaction strings daily. Walk me through the NLP and Machine Learning pipeline you would build to map 'UBER* EATS 02/14/23' to a clean 'Uber Eats' merchant entity, handling millions of minor textual variations. (Applied ML / NLP)
2We want to build a model to predict user churn during the Plaid Link flow (abandoning the bank login process). How would you define 'churn' quantitatively, and what features would you engineer? (Feature Engineering / Impact)
3Write a SQL query to identify all distinct users who successfully connected a bank account, but then never generated an API call from the host app within the following 30 days. (SQL / Data Manipulation)
4A product manager wants to run an A/B test on a new Plaid Link UI, but the sample size of users hitting the new UI is incredibly small due to a slow rollout. How do you advise them statistically? (A/B Testing / Rigor)
5Tell me about a time your data model was highly accurate in a Jupyter notebook but completely failed to scale or perform adequately when deployed to production. (Engineering Collaboration)
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