Went through the Wells Fargo machine learning engineer interview process earlier this year. Role was on their risk and fraud detection team in San Francisco. Sharing because most ML interview content online is either FAANG-focused or generic. Banks are different.
What I expected: heavy leetcode grind, vague ML system design, maybe some statistics.
What I actually got: substantially more applied and domain-specific than I anticipated.
Process overview: Recruiter screen. Standard. ML coding screen (60 min). They gave me a real dataset (anonymized transaction records) and asked me to build and evaluate a simple fraud classifier in Python. Jupyter notebook environment. They cared about: how I handled class imbalance, which metric I optimized (they pushed back when I defaulted to accuracy, which was the right pushback), and how I would explain the model to a compliance team. That last part was the actual test. XGBoost with SHAP values came up in my answer and they asked follow-up questions about feature importance interpretation. ML system design (45 min). How would you design a real-time fraud scoring system that has to make a decision in under 200ms, handle concept drift, and log everything for regulatory audit? This is the kind of problem that's very specific to financial services. I talked through feature stores, model serving infrastructure, shadow mode testing, and the challenge of retraining on labeled data that takes weeks to arrive (chargebacks are slow). They seemed to appreciate that I'd thought about the label delay problem. Behavioral panel. Two rounds. They really wanted STAR-method answers and were explicit about it. They asked about working with stakeholders who didn't understand model limitations, and handling a model failure in production.
What mattered most: domain knowledge around financial services ML. If you've worked on fraud, credit risk, AML, or anything adjacent, highlight it aggressively. If you haven't, spend time reading about the problems. The general ML skills are table stakes. What differentiates candidates is whether they understand why precision/recall tradeoffs in fraud detection have real business consequences.
Leveling landed me at what they call Senior Data Scientist II (the ML eng titles at WF are a bit unusual). Base around $165k in SF, bonus target 15%, RSUs over 3 years. Not FAANG total comp, but the role had real scope.