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A practical plan for a time-limited take-home assignment
A take-home assignment needs a clear answer, evidence that supports it, and a submission another person can run. The difficult part is deciding what to finish within the stated time. This guide offers a four-hour example schedule; adapt it to the employer's instructions and your available time. For a complete analysis with data and output, see the worked delivery case.
1. Agree on the question and budget your time
Before opening a notebook, write down the decision, required deliverables, deadline and time zone, expected effort, and evaluation criteria. Confirm permitted tools, outside help, and AI use. Ask a focused clarification if the metric or population is ambiguous. If no answer arrives, document a reasonable assumption and show how it affects the answer.
| Minutes | Work | Finished output |
|---|---|---|
| 0–20 | Read the brief and define scope | Question, assumptions, and acceptance checklist |
| 20–65 | Inspect and validate the data | Data-quality notes and a reproducible input step |
| 65–140 | Produce the core answer | Baseline analysis and checked results |
| 140–180 | Challenge the answer | One useful sensitivity or validation check |
| 180–220 | Write the report | Recommendation, evidence, and limitations |
| 220–240 | Run and package everything | Clean execution and correct submission files |
These blocks total four hours; they are a planning suggestion, not a hiring standard. If a required step overruns, cut an optional chart or tuning experiment. Protect the final execution and packaging time.
2. Use a familiar language and make execution obvious
Use the requested language; when there is a choice, use the one in which you can test and explain the work. Give variables useful names and put assumptions beside the relevant calculation. Comments should explain decisions, units, and exceptions. A comment repeating every line of code adds little.
Keep one obvious entry point. Record language and package versions, relative file paths, and the command or notebook sequence needed to reproduce the report. A fresh run must not depend on cells executed out of order or data loaded in your earlier session.
3. Inspect data before changing it
Establish row meaning, key uniqueness, date coverage, units, missingness, and join cardinality. Keep raw inputs unchanged. Record exclusions and their counts. A repeated customer ID may be legitimate when a row represents an order; deleting duplicates without a key definition can remove valid activity.
Do not fill every missing value with a mean. A missing delivery time is not an average delivery, and a missing target is not a known outcome. Report missingness first and choose treatment according to the question. If preprocessing is learned for a predictive model, fit it on training data only; scikit-learn's data-leakage guidance explains this distinction.
4. Choose a narrow answer you can defend
Play to your strengths within the brief. Strong visualization does not replace a required model, and an impressive model does not answer a request for a retention definition. Write a minimum complete answer in one sentence, then list the evidence needed to support it.
5. Establish a baseline before adding complexity
A descriptive task may need no model. For prediction, compare against an appropriate simple baseline and use a validation split that resembles deployment. Future forecasting needs separation by time; repeated observations from the same person may need separation by person. Ordinary random folds are not a universal default.
Choose a primary metric tied to the decision, plus a relevant failure check such as performance on a small segment. Limit candidate models and tuning. More cross-validation folds cost more computation; cross-validation is not inherently a time-saving technique. Leave the final test set out of model selection.
6. Connect results to a decision
Explain what someone could do next, what evidence justifies it, and what it costs to be wrong. Label estimated benefits as estimates. An offline classification score does not establish a revenue increase or show that the intervention will cause a change.
7. Keep extra findings subordinate to the brief
Include an unexpected finding only if it changes the decision or reveals a serious data issue. Put interesting but untested patterns in an appendix. Searching many segments increases the chance of finding an accidental pattern; disclose exploratory analyses rather than presenting each as a confirmed discovery.
8. Prefer a useful deliverable over an impressive stack
One reliable script, a small results table, and a short report can be sufficient. Avoid introducing a dashboard server, external API, or cloud dependency unless the task requires it. Do not upload employer-provided data to a public repository or external AI service without authorization.
9. Separate prediction, explanation, and causation
Check predictions on unseen data, but do not treat predictive accuracy as proof that coefficients or feature importance reveal causes. Correlated inputs, leakage, and selection effects can mislead interpretation. A descriptive estimate can still be useful without a strong predictive model when its population, uncertainty, and limitations are clear.
10. Submit a reviewable package
- A short README gives the question, file map, setup, and run instructions.
- The report states the recommendation before supporting tables.
- Every reported number can be traced to a calculation.
- Missing data, exclusions, and unfinished work are explicit.
- Code runs from a clean session and contains no credentials or unauthorized data.
- The requested files open correctly and arrive through the requested channel.
Prepare to discuss your choices
Be ready to explain your baseline, one alternative you rejected, and the next check you would run. A bounded, honest submission gives the interviewer something concrete to evaluate. Additional complexity is useful only when it improves the answer.
