Spokeo·Data Scientist·Onsite - Coding / Algorithms·Intermediate

Intermediate

Apr 2026

Summary

Onsite at Spokeo for a Data Scientist role where the hiring manager and VP sat in and ran me through a logic/puzzle gauntlet. Six AMC-8 style problems including the river-crossing classic. Not what I expected walking in.

Questions Asked (1)

Solve a set of six math and logic puzzles, including the farmer-fox-chicken-grain river-crossing problem, and explain the algorithm and why it works.

Algorithms & Data Structures

Author's notes

The river-crossing one I actually knew, but they wanted a formal proof of correctness, not just the answer.

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AI HintsAI Generated

Suggested Approach

Start by restating the problem and clarifying constraints (e.g., boat capacity, entities that cannot be left alone). For the farmer-fox-chicken-grain puzzle, model it as a state-space search where states represent the positions of the farmer and items, and transitions are valid moves that do not violate constraints. Then explain the algorithm (e.g., BFS) and why it guarantees a solution, and generalize to other puzzles by identifying similar state-space structures.

Pro tip: Demonstrate that you can abstract the puzzle into a graph problem and discuss trade-offs between different search algorithms (BFS vs DFS) and heuristics. Also, mention how this relates to real-world data science problems like constraint satisfaction and planning.

1. Understand and restate the problem

Clearly restate the farmer-fox-chicken-grain puzzle, listing all entities, the boat capacity, and the constraints (e.g., fox eats chicken, chicken eats grain if left unattended). Confirm any assumptions with the interviewer.

2. Model as a state-space search problem

Define states as tuples representing the positions of the farmer, fox, chicken, and grain (e.g., left or right bank). Define valid moves (farmer crosses with at most one item) and goal state (all on the right bank).

3. Choose and explain an algorithm

Select an appropriate search algorithm (e.g., BFS for shortest solution) and explain why it works: BFS explores states level by level, guaranteeing the shortest sequence of moves. Describe how to implement it (queue, visited set).

4. Solve and verify the solution

Walk through the solution steps (e.g., take chicken, return alone, take fox, bring chicken back, take grain, return alone, take chicken). Verify that no constraints are violated at any step.

5. Generalize and discuss other puzzles

Explain how the same approach applies to other puzzles (e.g., missionaries and cannibals, water jug problem). Highlight the common pattern: state-space search with constraints, and discuss potential optimizations or alternative algorithms (e.g., DFS with backtracking, A* with heuristics).

Key Points to Mention

State-space representation: define states, transitions, and goal test.
Breadth-first search (BFS) guarantees shortest solution in terms of number of moves.
Constraint checking: ensure no invalid state is reached (e.g., fox with chicken without farmer).
Algorithm complexity: time and space O(b^d) where b is branching factor and d is depth.
Generalization to other puzzles: missionaries and cannibals, water jug, etc.
Real-world applications: planning, constraint satisfaction, and search in AI.

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