How to assess · For hiring teams
How to Assess Machine Learning Skills When Hiring
The test formats that actually work for Machine Learning, what a strong answer looks like, sample questions and a scoring rubric you can use as-is.
The short answer
Assess Machine Learning with a task, not a conversation: case study on a realistic problem, review a flawed notebook, ai-scored assessment (e.g. cohesyve) or production conversation. Score it against written criteria you fix before you see any submissions, and weight the criteria that the role actually depends on.
- Frames the problem before modelling: what decision, what cost of error, what baseline
- Investigates the data first and finds the leakage, the missingness and the class imbalance
- Chooses evaluation metrics that match the decision, and validates with a split that respects time and grouping
- Prefers the simplest model that meets the requirement and can justify complexity when it is needed
Paste a job description; Cohesyve generates a role-specific assessment and rubric. Ten candidates free, no card.
Machine learning hiring is distorted by the gap between what candidates practise — model architectures, benchmark accuracy — and what the job requires: framing a problem so a model can help, building features from messy data, validating honestly, and shipping something that keeps working after the data drifts. Most interviews test the first list. This page covers how to assess machine learning as it is practised: problem framing, data and features, evaluation and leakage, deployment and monitoring, and the judgement to know when a model is not the answer.
Why Machine Learning is worth testing
The expensive ML failures are rarely about choosing the wrong algorithm. They are leakage that makes a model look excellent until it meets real data, evaluation on a metric that does not match the business decision, a model shipped without monitoring that degrades for months unnoticed, and projects that should have been a rule. Testing surfaces whether a candidate thinks about these before modelling, and that predicts whether their work reaches production and stays useful.
What strong Machine Learning looks like
- Frames the problem before modelling: what decision, what cost of error, what baseline
- Investigates the data first and finds the leakage, the missingness and the class imbalance
- Chooses evaluation metrics that match the decision, and validates with a split that respects time and grouping
- Prefers the simplest model that meets the requirement and can justify complexity when it is needed
- Plans deployment and monitoring — drift, performance, retraining — as part of the work
- Communicates uncertainty and limitations honestly to non-specialists
- Knows when a rule, a query or a dashboard would serve better than a model
Ways to assess Machine Learning
Case study on a realistic problem
Describe a business problem — churn, fraud, demand — with a small dataset and known traps (a feature computed after the outcome, a time-ordered split ignored). Ask for an approach, the pitfalls, the metric and a baseline. Sixty minutes, written or discussed.
Pros
Cons
Best for Any level; the traps scale it.
Review a flawed notebook
Provide a notebook with a leaky feature, a random split on time-series data, and a metric that ignores class imbalance. Ask what is wrong and what the real performance is likely to be.
Pros
Cons
Best for Mid and senior roles.
AI-scored assessment (e.g. Cohesyve)
Generate an ML scenario from the job description — a problem framing, a validation critique, a deployment plan — with a rubric. Each candidate receives a different variant; reasoning is scored in writing.
Pros
Cons
Best for Screening a pool before technical rounds.
Production conversation
Ask about a model they shipped: how it was monitored, what went wrong, and what they changed.
Pros
Cons
Best for Senior and applied roles.
Cohesyve
Run a Machine Learning assessment on your next opening
Cohesyve generates a unique Machine Learning task per candidate from your job description, with the scoring rubric attached. Questions are different for every applicant, so they cannot be shared or looked up.
What to test
Problem framing
Whether they build the right thing.
Data and leakage
Whether they find the traps.
Evaluation
Whether they measure what matters.
Deployment and monitoring
Whether the model survives contact with production.
Sample Machine Learning questions
What is data leakage, and how do you find it?
EntryLook for Information from the future or the target in the features; suspiciously good performance; check feature timing and correlations.
Why is accuracy a bad metric for a fraud model with 0.5% positives?
EntryLook for Predicting all-negative scores 99.5%; use precision, recall, PR-AUC, and tie to the cost of each error.
A model scores 0.95 AUC offline and performs poorly in production. What are the likely causes?
MidLook for Leakage, distribution shift, a validation split that did not respect time, or the offline metric not matching the decision.
How would you decide whether to build a model or a rule for a given problem?
MidLook for Baseline with a rule first; model only if it beats it enough to justify complexity, monitoring and maintenance.
Design the monitoring for a model that scores loan applications.
SeniorLook for Input drift, prediction distribution, delayed-label performance, fairness metrics, alert thresholds, retraining plan, and human review.
Red flags
- Jumps to model choice before understanding the data
- Cannot explain leakage
- Uses accuracy for imbalanced problems
- Has never monitored a model in production
- Cannot describe a project where the answer was not a model
Scoring rubric
| Criterion | Weight | What strong looks like |
|---|---|---|
| Framing and judgement | 25% | Right problem, right baseline, honest about whether ML is needed. |
| Data and leakage | 25% | Finds the traps before modelling. |
| Evaluation | 25% | Metrics and splits match the decision. |
| Production thinking | 15% | Monitoring and retraining are designed in. |
| Communication | 10% | Limitations are stated clearly to non-specialists. |
Mistakes hiring teams make
- Quizzing algorithm internals instead of judgement
- Not planting leakage — the most common real failure
- Rewarding complex models over honest baselines
- Skipping production and monitoring
- Hiring on Kaggle results without testing framing
Roles that need Machine Learning
Common questions
Should I test deep learning specifically?
Only if the role requires it. Framing, leakage, evaluation and monitoring apply to every model type and are where most failures come from.
What is the best single ML question?
Describe a model that scored well offline and failed in production and ask why. It tests leakage, validation and shift awareness together.
How do I assess ML if I am not an ML specialist?
Use a case with known traps and a rubric; the reasoning about leakage and metrics is legible to a technical non-specialist. Bring in an external reviewer for finalists.
How long should an ML assessment take?
Sixty minutes for a case; two to three hours capped for a notebook-based take-home with a written summary.
Cohesyve · Skill assessments for hiring
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