Sample Brief · representative example, not a real user

Data Analyst · Task Exposure Map

Data Analyst AI displacement risk — 3 moves to make in 90 days

Text-to-SQL and AI dashboard generation now match senior-analyst speed on routine pulls; pure-reporting analyst job postings are in measurable decline (Lightcast 2024; Anthropic Economic Index 2025). The defensible analyst is one who designs the experiment, defines the metric, and presents the result with stakes attached.

Full data analyst role page Calls Brain accuracy table How we calculate this

Triggering signals

What the data shows for this role

Text-to-SQL and AI dashboard generation at parity with senior-analyst speed for routine pulls (Lightcast 2024 — analyst job-posting decline; Anthropic Economic Index 2025).

Companies are shifting "analyst" headcount toward "analytics engineering" (dbt, data contracts) where modelling judgment is the moat.

Org-specific metric definitions, A/B test design under stakes, causal inference, stakeholder synthesis, and decision-room presence remain weakly automatable.

Signals quoted verbatim from Career Runway’s career-moves rubric (V11.1). Each references peer-reviewed research, independent benchmarks, or large-N labour-market data — see citations on each move below.

Confidence band

AI exposure

42–58

grade C

⚠ Placeholder · Sample brief — confidence band shown is a representative midpoint, not an evidence-grounded computation. Take the assessment for a calibrated number.

3 Primary Moves

Highest-defensibility moves for the next 90 days

Verbatim from the career-moves rubric. Each move ships an observable artefact — not self-report.

Move 1 · Defensibility

Take ownership of metric definitions with VP-level signoff

High defensibility

Why it works · metric ownership is a slow-moving moat; once your definitions are referenced in board decks, replacing you is expensive.

Time budget

4h / week

Prerequisites

cross-functional access, executive sponsorship

Verifiable artefact

metric dictionary in version control with dated VP sign-offs; quarterly review log; metric IDs cited in ≥3 leadership decks.

Citation: O*NET 4.A.2.a.2 (Evaluating Information to Determine Compliance with Standards)

Move 2 · Defensibility

Build a documented experimentation programme with decision log

High defensibility

Why it works · experiment-design judgment under stakes is precisely what LLMs cannot replace; the decision log compounds.

Time budget

5h / week

Prerequisites

experimentation platform, partnership with PM/eng

Verifiable artefact

≥6 experiments shipped via documented framework with hypothesis, sample-size calc, and decision; decision log linked from team handbook.

Citation: Kohavi, Tang & Xu 2020 — Trustworthy Online Controlled Experiments

Move 3 · Skill build

Re-skill into causal inference (geo holdouts, synthetic controls, diff-in-diff)

High defensibility

Why it works · methodology depth that finance and growth teams trust is rare; few analysts can build it.

Time budget

5h / week

Prerequisites

SQL fluency, basic Python or R

Verifiable artefact

notebook or repo with reproducible synthetic-control or geo-holdout study; methodology memo circulated; one shipped business decision references it.

Citation: Athey & Imbens 2017 — Econometric causal inference

Tracking layer

What execution looks like — representative example

Sample data, not a real user. The tracking layer in the product follows actual move-completion artefacts and recomputes defensibility from observed signals.

Daniel completed Move 1 — “Take ownership of metric definitions with VP-level signoff” — over 6 weeks. Tracking-layer artefact recorded: metric dictionary in version control with dated VP sign-offs; quarterly review log; metric IDs cited in ≥3 leadership decks.

Defensibility

→

▲2 pts after artefact verification

Sample numbers. Real movement is computed from your task map + verified move artefacts when you complete an assessment.

Get your own Brief

Your moves depend on
your actual task mix.

This sample is the archetype for a data analyst. Your specific tooling, environment, and tenure produce a different Task Exposure Map — and a different ordered list of moves. About 10 minutes, free, no card.

Map my exposure View methodology

Other sample briefs

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Sample Brief · representative example, not a real user

Data Analyst · Task Exposure Map

Data Analyst AI displacement risk — 3 moves to make in 90 days

Full data analyst role page Calls Brain accuracy table How we calculate this

Triggering signals

What the data shows for this role

Text-to-SQL and AI dashboard generation at parity with senior-analyst speed for routine pulls (Lightcast 2024 — analyst job-posting decline; Anthropic Economic Index 2025).

Companies are shifting "analyst" headcount toward "analytics engineering" (dbt, data contracts) where modelling judgment is the moat.

Org-specific metric definitions, A/B test design under stakes, causal inference, stakeholder synthesis, and decision-room presence remain weakly automatable.

Confidence band

AI exposure

42–58

grade C

⚠ Placeholder · Sample brief — confidence band shown is a representative midpoint, not an evidence-grounded computation. Take the assessment for a calibrated number.

3 Primary Moves

Highest-defensibility moves for the next 90 days

Verbatim from the career-moves rubric. Each move ships an observable artefact — not self-report.

Move 1 · Defensibility

Take ownership of metric definitions with VP-level signoff

High defensibility

Why it works · metric ownership is a slow-moving moat; once your definitions are referenced in board decks, replacing you is expensive.

Time budget

4h / week

Prerequisites

cross-functional access, executive sponsorship

Verifiable artefact

metric dictionary in version control with dated VP sign-offs; quarterly review log; metric IDs cited in ≥3 leadership decks.

Citation: O*NET 4.A.2.a.2 (Evaluating Information to Determine Compliance with Standards)

Move 2 · Defensibility

Build a documented experimentation programme with decision log

High defensibility

Why it works · experiment-design judgment under stakes is precisely what LLMs cannot replace; the decision log compounds.

Time budget

5h / week

Prerequisites

experimentation platform, partnership with PM/eng

Verifiable artefact

≥6 experiments shipped via documented framework with hypothesis, sample-size calc, and decision; decision log linked from team handbook.

Citation: Kohavi, Tang & Xu 2020 — Trustworthy Online Controlled Experiments

Move 3 · Skill build

Re-skill into causal inference (geo holdouts, synthetic controls, diff-in-diff)

High defensibility

Why it works · methodology depth that finance and growth teams trust is rare; few analysts can build it.

Time budget

5h / week

Prerequisites

SQL fluency, basic Python or R

Verifiable artefact

notebook or repo with reproducible synthetic-control or geo-holdout study; methodology memo circulated; one shipped business decision references it.

Citation: Athey & Imbens 2017 — Econometric causal inference

Tracking layer

What execution looks like — representative example

Sample data, not a real user. The tracking layer in the product follows actual move-completion artefacts and recomputes defensibility from observed signals.

Defensibility

→

▲2 pts after artefact verification

Sample numbers. Real movement is computed from your task map + verified move artefacts when you complete an assessment.

Get your own Brief

Your moves depend on
your actual task mix.

Map my exposure View methodology

Other sample briefs

What the data shows for this role

Highest-defensibility moves for the next 90 days

Take ownership of metric definitions with VP-level signoff

Build a documented experimentation programme with decision log

Re-skill into causal inference (geo holdouts, synthetic controls, diff-in-diff)

What execution looks like — representative example

Your moves depend onyour actual task mix.

What the data shows for this role

Highest-defensibility moves for the next 90 days

Take ownership of metric definitions with VP-level signoff

Build a documented experimentation programme with decision log

Re-skill into causal inference (geo holdouts, synthetic controls, diff-in-diff)

What execution looks like — representative example

Your moves depend onyour actual task mix.

Your moves depend on
your actual task mix.

Your moves depend on
your actual task mix.