Take-Home Tactics — The Hidden Rubric

Most DA roles include a take-home. You're given a dataset and 2–5 days. The rubric is rarely shared. It's almost always:

Did you answer the question that was asked? (40%)
Is your code clean and reproducible? (20%)
Did you communicate the result clearly? (25%)
Did you go beyond the basic ask? (15%)

If you optimize for those four, you score much higher than candidates who spent the same time on a fancier model.

(1) Answer the question that was asked

Read the prompt twice. Most candidates answer a related question — usually a more interesting one — instead of the actual one asked. Don't be that candidate.

If the question is "what factors are correlated with churn?", don't build a predictive model. They asked for correlation, not prediction.

Re-read the prompt at the end of your analysis to make sure your answer matches. If your write-up's headline doesn't directly answer the question, rewrite the headline.

(2) Clean, reproducible code

Single notebook or script that runs end-to-end without manual steps.
Clear cell/section structure: load data → clean → explore → analyze → conclude.
Variable names that mean something (active_users, not df_temp_3).
A README with 2 lines: "how to run this" and "what's in this".

Reviewers grade reproducibility heavily — if your code doesn't run on their machine, they DQ you regardless of the analysis quality. Test it in a clean Python environment before submitting.

(3) Clear communication

A 1-page summary at the top of your submission. The summary should answer:

What was the question?
What's the answer? (Lead with this.)
How confident are you? Why?
What would you do next if you had more time?

This 4-bullet summary is what the hiring manager reads first. Often it's all they read. If your headline answer is buried on page 7, you've already lost.

(4) Going beyond — the right way

The temptation: more charts, fancier models. The right move: domain insight.

If the data shows churn correlates with low usage, don't stop there. Suggest which usage thresholds matter, ranked by lift on churn.
If signups are dropping, don't just say "investigate". Pre-run the most likely hypotheses and rule some out.
Spot something the prompt didn't ask about but is interesting. Mention it in 2 sentences under "Bonus observations".

Going beyond looks like "I noticed X while doing the main analysis — worth investigating later." Not "here's a 5-model ensemble I built unprompted."

Time allocation (for a 4-day take-home)

Day 1: Read the prompt twice. Load data. EDA. Form 2–3 hypotheses.
Day 2: Main analysis. Get to a draft answer.
Day 3: Refine. Add the bonus observations. Clean code.
Day 4: Write the summary. Test reproducibility. Re-read the prompt. Submit.

Candidates who burn day 1 on EDA and day 4 on the model usually fail to write a tight summary. Inverted: start with the answer, polish backwards.

The submission checklist

Before you hit send:

Headline answer matches the prompt's question word-for-word.
1-page summary at the top.
Code runs end-to-end in a fresh environment.
No commented-out cells, no exploratory junk in the final notebook.
One "bonus" observation worth 2 sentences.
README with 2 lines.

Takeaway

A take-home is a structured test, not an open creative writing prompt. Hit the rubric. Save the creativity for the bonus observations.