Graham Capital·Software Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Graham Capital software engineering interview that leaned heavily into the modeling and validation side of things. One question in particular felt more like a quant research interview than an SWE screen.

Questions Asked (1)

For a project you've worked on, walk through your key assumptions, how you justified them, how you validated each one (through EDA, sensitivity analysis, ablations, or out-of-sample testing), the full pipeline from data collection to final evaluation, and how you avoided data leakage and overfitting.

A/B Testing & ExperimentationRoot Cause AnalysisTechnical Trade-offs

Author's notes

This was a lot to unpack in one question.

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AI HintsAI Generated

Suggested Approach

Select a project where you made deliberate modeling or design choices, then structure your answer as a narrative that moves from problem framing and assumptions through validation and pipeline to final evaluation. Emphasize how you proactively guarded against leakage and overfitting at each stage, and quantify the impact of your validation steps.

Pro tip: For a quant firm like Graham Capital, highlight how you used out-of-sample testing and sensitivity analysis to ensure robustness, and mention any time-series-specific techniques (e.g., walk-forward validation) if applicable. Show that you think about assumptions as risks that need continuous monitoring, not just one-time checks.

1. Set the context and state assumptions

Briefly describe the project goal and list the key assumptions you made about the data, model, or business logic. Explain why each assumption was necessary and how you documented them.

2. Justify and validate assumptions

Explain how you justified each assumption using domain knowledge or initial EDA, and describe the specific validation techniques (e.g., sensitivity analysis, ablations, out-of-sample tests) you used to confirm or refute them.

3. Walk through the pipeline

Outline the end-to-end pipeline: data collection, cleaning, feature engineering, model training, and evaluation. Highlight any critical design decisions and how they addressed potential leakage or overfitting.

4. Detail leakage and overfitting prevention

Describe concrete steps taken to avoid data leakage (e.g., time-based splits, avoiding target encoding before split) and overfitting (e.g., cross-validation, regularization, early stopping). Mention how you monitored for these issues.

5. Summarize results and lessons learned

Present the final evaluation metrics and business impact, then reflect on what you would do differently and how you ensured the solution generalized beyond the training data.

Key Points to Mention

Use of out-of-sample testing and walk-forward validation for time-series data
Sensitivity analysis to test robustness of assumptions
Ablation studies to quantify feature importance
Techniques to prevent data leakage: proper train/validation/test splits, avoiding preprocessing on full data
Overfitting mitigation: regularization, cross-validation, early stopping
Documentation and monitoring of assumptions throughout the project lifecycle

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