Classic graph traversal problem and I knew it, but I still took a minute to settle on BFS vs DFS.
Clarify the definition of a 'distinct island' (e.g., shape-based vs. just connected components) and then use BFS/DFS to traverse each connected component, marking visited cells. If distinct shapes are required, normalize each island's coordinates and store them in a set to count unique shapes.
Pro tip: Discuss trade-offs between DFS and BFS (e.g., recursion depth vs. queue memory) and mention that for very large grids, an iterative approach avoids stack overflow. Also, highlight how to handle edge cases like empty grids or all-water grids.
Ask whether 'distinct islands' means simply connected components or unique shapes. Confirm grid dimensions, cell values, and connectivity (4-directional vs. 8-directional).
Select BFS or DFS to explore each island. Explain your choice based on grid size and memory constraints.
Iterate through each cell; when land is found, start traversal to mark all connected land cells as visited (e.g., set to '0' or use a visited matrix).
If counting components, increment a counter for each traversal. If counting distinct shapes, record the relative coordinates of each island and add to a set.
State time complexity O(M*N) and space complexity O(M*N) for visited storage or recursion stack, and discuss potential optimizations.
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Start by clarifying the problem and identifying the monotonic property that enables binary search. Then, design a greedy check function that verifies if a candidate solution is feasible, and use binary search to find the optimal value. Finally, analyze the time complexity and test edge cases.
Pro tip: Always articulate the monotonicity condition clearly and explain why greedy works for the check function. This demonstrates deep understanding and helps avoid off-by-one errors.
Ask clarifying questions to ensure you understand the problem constraints and requirements. Restate the problem in your own words to confirm with the interviewer.
Determine the property that is monotonic (e.g., if a solution is feasible for a value, it's feasible for all larger values). Define the search space (low and high bounds) for binary search.
Develop a greedy algorithm that, given a candidate value, checks if it's possible to achieve the goal. Explain why greedy is optimal for this check (e.g., exchange argument).
Integrate the greedy check into a binary search loop. Handle edge cases and ensure the binary search terminates correctly.
Discuss time and space complexity. Walk through test cases, including edge cases, to validate the solution.
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Structure your answer as a concise narrative that connects your past experiences to the role's requirements, emphasizing adaptability and comfort with ambiguity. Highlight specific projects where you navigated unclear problems, and tie them to the skills Optiver values in data scientists.
Pro tip: Quantify your impact wherever possible (e.g., 'reduced latency by 30%') and explicitly mention how you thrived in ambiguous situations, as Optiver is a trading firm that values quick, data-driven decisions under uncertainty.
Start with a one-sentence summary of your current role and years of experience, setting the stage for your narrative.
Mention relevant degrees, certifications, or early projects that built your foundation in data science, focusing on analytical and problem-solving skills.
Describe 2-3 significant projects, emphasizing the problem, your approach, and measurable outcomes. Choose examples that showcase adaptability to new domains or ambiguous requirements.
Explicitly link your background to Optiver's needs, such as experience with real-time data, financial modeling, or working in fast-paced environments.
Summarize what you've been working on recently and express enthusiasm for applying your skills to challenges in trading and market making.
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Use the STAR method to structure your response, focusing on a specific situation where you navigated ambiguity or resolved a conflict. Explicitly tie your actions and results to Amazon's Leadership Principles, such as 'Customer Obsession' or 'Have Backbone; Disagree and Commit'. Keep your answer concise and highlight your decision-making process and learnings.
Pro tip: Amazon interviewers evaluate whether your story demonstrates the Leadership Principle in action, not just the outcome. Choose a story where you made a tough call or influenced others without authority, and quantify the impact to show you're data-driven.
Choose a past experience that clearly showcases adaptability in ambiguity or conflict resolution, ideally with measurable results. Ensure the story aligns with at least one Amazon Leadership Principle.
Briefly describe the situation and your responsibility, providing enough detail for the interviewer to understand the stakes. Avoid excessive background; focus on the challenge.
Explain the specific steps you took to address the ambiguity or conflict, emphasizing your thought process, collaboration, and any trade-offs. Use 'I' statements to clarify your role.
Quantify the outcome (e.g., time saved, revenue increased, team morale improved) and connect it back to the Leadership Principle. Mention any lessons learned or how you applied feedback.
Explicitly state which Leadership Principle(s) your story demonstrates and why it matters for the role. This shows you understand Amazon's culture and values.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.