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Lead Bank·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Coding round at Lead Bank for a software engineer role. The problem was stock prices, broken into four parts, with the last part being optional to just talk through rather than actually code.

Questions Asked (1)

Q1

Solve a stock prices problem across multiple parts (four total), with the final part only requiring a verbal explanation rather than a coded solution.

Algorithms & Data Structures
Author's notes

Four-parter on stock prices.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases for each part, then solve them in increasing order of complexity, explaining your thought process as you go. For the final part, focus on articulating the algorithmic intuition and trade-offs rather than writing code.

Pro tip: Explicitly state the time and space complexity of each solution and discuss potential optimizations, showing you think beyond just correctness. For the verbal part, structure your explanation like a story: problem, approach, complexity, and edge cases.

1. Clarify Requirements

Ask clarifying questions about input size, constraints, and expected output for each part to ensure you understand the problem fully.

2. Solve Incrementally

Tackle each part in order, starting with the simplest (e.g., single transaction) and building up to more complex scenarios (e.g., multiple transactions, with cooldown).

3. Explain and Optimize

For each coded solution, walk through your logic, then analyze time and space complexity and suggest possible optimizations.

4. Verbal Deep Dive

For the final part, describe the algorithm conceptually, including state definitions, transitions, and why it works, without writing code.

5. Test and Validate

Run through edge cases (e.g., empty array, decreasing prices) and validate your solutions against them.

Key Points to Mention

  • Dynamic programming state definitions and transitions for each variation
  • Time and space complexity trade-offs (e.g., O(n) vs O(n^2), space optimization)
  • Edge cases such as no profit possible, single day, or large input sizes
  • Greedy vs DP approaches and when each is appropriate
  • Handling constraints like cooldown or transaction fees
  • Clear communication of thought process and assumptions

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