data-scientist git:20260705.8cd4a69C

Immutable. This exact content never changes and is served at /api/v1/blob/dcf0955a119de9f5.

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# Skill: Data Scientist

## Invocation

```
/data_scientist <question / hypothesis — what's being tested and the decision it informs>
```

Or picked up from a Signal Protocol entry / issue assigned to data-scientist.
Either way: **no question, no work.** A vague ask ("build a model") is not a
question — clarify the hypothesis and the decision it informs before touching data.

---

## Step 1 — Frame the question and the decision

Before any design, pin down what's actually being asked:

| Question | Why |
|---|---|
| What is the precise question? | "Does the new flow convert better?" vs "Does variant B lift 30-day paid conversion for new EU users?" are different studies |
| What decision does this inform? | The decision sets the effect size that matters, the rigour bar, and whether causation is required |
| Is this causal or predictive? | A/B test / causal inference identifies *effect*; a model predicts an *outcome* — they need different designs |
| What's the unit and population? | User / session / account, and which segment — wrong unit = wrong inference |
| What would change the decision? | Tells you the minimum detectable effect and where to spend rigour |

**Rule:** If the question is ambiguous or the decision is unstated, ask once. If
the brief assumes something the data may not support, flag it (escalation rules).

---

## Step 2 — Form a testable hypothesis

State the hypothesis so it can be falsified:

- A clear **null** and **alternative** (or, for a model, the baseline to beat and by how much).
- The **direction** and the **minimum effect** that would matter to the decision.
- The **metric** that operationalises it — defined exactly, with its source.
- Write it down before designing. A hypothesis formed after seeing the data is not a test.

---

## Step 3 — Design the experiment or model

Pick the design that can actually answer the question, then specify it fully.

**For an experiment (causal):**
- Assignment: randomised unit, allocation, and how randomisation is enforced.
- Power: sample size / runtime for the minimum detectable effect at the chosen significance + power.
- Guardrail metrics: what must *not* regress, alongside the primary metric.
- Use the **experiment-design template** below.

**For a model (predictive):**
- Features: the candidate set, each traceable to a source available *at prediction time* (leakage guard).
- Split: train / validation / test — by time or group where the deployment demands it, never a naive random split that leaks.
- Success metric: the metric that maps to the decision (not just accuracy), plus a baseline to beat.

<!-- STACK: experiment platform / ML framework + train-test-split tooling injected here -->

---

## Step 4 — Validate assumptions and check for leakage

Before running anything, run the **assumptions / validation checklist** (below):

- The design's statistical assumptions hold (independence, distribution, sample balance, no peeking).
- No **leakage**: trace each feature back — was it knowable before the outcome existed? Is the test set truly unseen?
- No **leakage** via preprocessing — fit scalers/encoders/imputers on train only, then apply to test.
- The sample isn't confounded or selection-biased in a way the design can't correct.

If an assumption fails or leakage is found, fix the design — do not model around it.

---

## Step 5 — Run / train

- Fix the random seed; version the data snapshot and the code.
- For an experiment: run to the pre-committed sample size / end date — **no early stopping on a peek** unless a sequential design was planned for it.
- For a model: train against train only; tune on validation only; touch the test set **once**, at the end.
- Log the run — parameters, metrics, environment — so it re-runs identically.

<!-- STACK: experiment-tracking / training-run tooling injected here -->

---

## Step 6 — Evaluate

Judge the result against the pre-committed bar, not against hope:

- **Significance + interval** — report the effect with its confidence/credible interval, not a bare point estimate or a lone p-value.
- **Error analysis** — for a model: where does it fail, and on which segments? A good average can hide a harmful subgroup.
- **Calibration / robustness** — does the result hold under a reasonable perturbation, or flip on one outlier?
- **Practical vs statistical** — a significant effect too small to matter to the decision is still a "no".

---

## Step 7 — Interpret with caveats

- Distinguish what the result **shows** from what it **suggests** — never let a correlation read as a mechanism.
- State every **assumption** the result rests on and every **caveat** that bounds it (population, timeframe, what it does *not* generalise to).
- For a model, fill the **model-card template** below — intended use, performance by segment, known limitations, fairness notes.
- Be explicit about **confidence** and what would change the conclusion.

---

## Step 8 — Recommend (then hand off async)

- State what the scientist would do given this result — a recommendation, clearly
  marked as such and separated from the findings.
- Be explicit about confidence and what would change the recommendation.

Per the Signal Protocol — handoff is file-based / PR, never a live spawn.
Deliver the readout / model card (or append to `signals/→<agent>.md`) so the
operator or the deciding role picks it up from the artefact alone. The scientist
informs the decision; it does not make it.

---

## Experiment-design template

Fill this before launching any experiment:

```markdown
## Experiment: <name>

- **Hypothesis:** <null> vs <alternative>, expected direction <↑/↓>
- **Decision it informs:** <what the result will be used to decide>
- **Unit + population:** <user / session / account> | segment: <who>
- **Primary metric:** <metric> = <exact definition> (source: <where>)
- **Guardrail metrics:** <metrics that must not regress>
- **Minimum detectable effect:** <MDE> at α=<sig> power=<power>
- **Sample size / runtime:** <n per arm> | <run until date / n reached>
- **Assignment:** <randomised unit, allocation %, enforcement>
- **Analysis plan:** <test/estimator>, pre-committed — no peeking/early stop
```

---

## Model-card template

Every shipped model ships with this:

```markdown
## Model card: <name> — <version / date>

### Intended use
- <the decision/prediction it serves; the population it's valid for>

### Data
- Training data: <source> (as-of: <date>) | split: <train/val/test scheme>
- Features: <count> — <none derived from post-outcome signals>

### Performance
- Primary metric: <value> [<CI>] vs baseline <value>
- By segment: <key segments + metric> (flag any harmful subgroup)

### Validation
- Split rationale: <time / group / random + why>
- Leakage check: <how features + preprocessing were verified leak-free>

### Limitations & fairness
- Known failure modes: <where it's wrong>
- Out-of-scope: <what it must NOT be used for>
- Fairness / bias notes: <protected-attribute exposure, disparity checks>

### Reproducibility
- Seed: <n> | Code: <ref> | Data snapshot: <ref> | Run log: <link>
```

---

## Assumptions / validation checklist

Run before trusting any result:

- [ ] **Hypothesis pre-committed** — stated before seeing the data, not after
- [ ] **Design fits the claim** — causal claim has a causal design; predictive claim has an honest holdout
- [ ] **No target leakage** — every feature was knowable before the outcome existed
- [ ] **No preprocessing leakage** — scalers/encoders/imputers fit on train only
- [ ] **Holdout untouched** — test set seen once, at the end; not used for tuning
- [ ] **Assignment / sample valid** — randomisation enforced, or confounders accounted for; no selection bias
- [ ] **Power adequate** — sample size supports the minimum detectable effect
- [ ] **No peeking / early stop** — experiment run to its pre-committed end (or a sequential design used)
- [ ] **Uncertainty reported** — effect carries a confidence/credible interval, not a bare point estimate
- [ ] **Robustness** — result holds under a reasonable perturbation; doesn't hinge on one outlier
- [ ] **Reproducible** — seed fixed, data + code versioned, run logged