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Bank of America·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Bank of America Data Scientist interview that included a coding round where they asked you to walk through your solution in detail afterward, covering complexity, alternatives, and trade-offs. More of a technical deep-dive than a pure coding test.

Questions Asked (1)

Q1

Walk through the logic of your coding solution step by step, analyze its time and space complexity, propose at least one alternative approach, and explain why you chose your solution over the alternative.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This was the part I was least prepared for.

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AI HintsAI Generated

Suggested Approach

Start by clearly restating the problem and the chosen solution, then walk through the code logic step by step, explicitly stating the time and space complexity. Next, describe at least one alternative approach, compare trade-offs, and justify why your solution is preferable for the given context, especially considering data science and banking constraints.

Pro tip: Quantify the trade-offs: e.g., 'The alternative uses 30% less memory but is 2x slower; since our dataset fits in memory and latency is critical, I chose the faster solution.' This shows you think in terms of business impact, not just theory.

1. Clarify the problem and solution

Restate the problem in your own words and briefly summarize your chosen approach. This ensures alignment and sets the stage for the detailed walkthrough.

2. Walk through the logic step by step

Explain the code or algorithm sequentially, using plain language and avoiding jargon. Highlight key operations, data structures, and control flow.

3. Analyze time and space complexity

Derive Big-O complexity for both time and space, explaining the dominant operations. Mention best, average, and worst cases if relevant.

4. Propose an alternative approach

Describe at least one different algorithm or method to solve the same problem. Outline its logic and its time/space complexity.

5. Compare and justify your choice

Contrast the two approaches on complexity, readability, scalability, and domain-specific constraints (e.g., data size, latency, interpretability). Explain why your solution is the best fit.

Key Points to Mention

  • Time and space complexity with Big-O notation, including best/average/worst cases
  • Trade-offs between approaches: speed vs. memory, simplicity vs. scalability
  • Domain relevance: how the solution handles financial data characteristics (e.g., large datasets, real-time processing, regulatory constraints)
  • Code readability and maintainability for team collaboration
  • Edge cases and potential optimizations (e.g., early termination, caching)
  • Business impact: how the choice affects model performance, deployment, or cost

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.