How data science teams use Codex

How data science teams use Codex | OpenAI

May 15, 2026

OpenAI Academy

How data science teams use Codex

See how data science teams can use Codex to turn questions, dashboards, and raw data into review-ready analysis assets.

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With Codex, data science teams can turn scattered inputs into usable analysis assets faster. Starting from dashboards, metric definitions, exports, experiment notes, and business context, Codex helps assemble a first draft of the deliverable—including charts, caveats, source links, and review questions—so teams can validate the work and share it with confidence.

Learn more about using Codex for everyday work in our on-demand webinar⁠.

Top Codex use cases for data science teams

Most data science work does not end with the query. It ends with an artifact someone can read, challenge, and act on. Use these prompts to have Codex turn dashboards, exports, metric definitions, and stakeholder context into a first draft of a real deliverable—whether that’s a root-cause brief, impact readout, KPI memo, or dashboard spec. Then apply your judgment where it matters most: validating the evidence, pressure-testing the caveats, and sharpening the recommendation.

1. KPI root-cause analysis

Use this when: A key metric moved unexpectedly and the team needs a source-backed brief that explains what changed, why it likely happened, and what to do next.

What you bring

What Codex returns

KPI dashboard, metric definitions, exports, launch or campaign context, segment cuts, and relevant stakeholder threads

A root-cause brief with charts, confirmed drivers, hypotheses, caveats, source links, open questions, and recommended actions

Suggested plugins: Google Drive, Spreadsheets, Slack, Gmail, Documents

How it works

1. Codex reviews the metric definition, dashboard context, source exports, and recent business activity. 2. It breaks down movement by segment, cohort, channel, geography, and product surface where relevant. 3. It creates a review-ready root-cause brief that separates confirmed findings from hypotheses.

Starter prompt

Investigate why [KPI] changed for [business/product/segment] during [time period]. Use the KPI dashboard, metric definitions, recent launch or campaign notes, customer or usage segments, spreadsheet exports, and collaboration threads I provide. Break down likely drivers by segment, cohort, channel, geography, and product surface where relevant. Create a root-cause brief with charts, caveats, source links, recommended actions, and open questions. Separate confirmed findings from hypotheses.

##### Real-world example

Investigate why weekly paid subscriptions changed for Acme Pro and Acme Plus. Use the “Subscriptions KPI Dashboard,” “April Growth Launch Notes,” metric definitions from “Consumer Metrics Glossary,” recent growth-metrics discussion notes, subscription warehouse exports, and any related context I provide. Create an executive root-cause brief with likely drivers, supporting charts, segment cuts, caveats, recommended actions, and source links. Validate the numbers and flag anything uncertain.

2. Business impact readout

Use this when: A launch, experiment, or initiative needs a clear readout leaders can use to decide whether to scale, adjust, or stop.

What you bring

What Codex returns

Experiment plan, success metrics, cohort data, dashboard exports, customer signals, and launch notes

A business impact readout with lift, guardrails, segment findings, methodology notes, caveats, and a recommendation

Suggested plugins: Google Drive, Spreadsheets, Slack, Gmail, Documents, Presentations

How it works

1. Codex reviews the initiative plan, success metrics, cohorts, dashboards, and customer signals. 2. It quantifies impact, checks guardrails, and inspects segment-level differences. 3. It creates a decision-ready readout with charts, caveats, methodology notes, and scale/change/stop guidance.

Starter prompt

Measure whether [initiative/experiment/launch] improved [target outcome]. Use the experiment or launch plan, success metrics, relevant dashboards, cohort or assignment data, customer signals, and launch notes I provide. Quantify the lift or movement, check guardrail metrics, inspect segment differences, and explain whether the team should scale, change, or stop the initiative. Return a business impact readout with charts, methodology notes, caveats, source links, and a clear recommendation.

##### Real-world example

Measure whether Acme’s April onboarding experiment improved activation. Use the “April Onboarding Experiment Plan,” experiment results export, onboarding funnel dashboard, customer cohort table, launch notes, and related team discussion context. Write a business impact readout with lift, guardrail metrics, segment differences, whether to scale or change the experiment, and the analysis steps used. Separate confirmed results from interpretation.

3. Analytics request agent

Use this when: A stakeholder ask is broad, ambiguous, or underspecified and needs to become a scoped analysis asset.

What you bring

What Codex returns

Stakeholder request, business context, metric glossary, source exports, dashboard links, and request threads

A scoped analysis plan plus stakeholder-ready answer with charts, caveats, source links, validation notes, and open questions

Suggested plugins: Google Drive, Spreadsheets, Slack, Gmail, Documents

How it works

1. Codex reviews the request, business question, metric definitions, available data, and surrounding context. 2. It scopes the analysis, identifies missing inputs, and runs a first pass using the provided data. 3. It creates a stakeholder-ready analysis asset with charts, caveats, validation notes, and analyst review questions.

Starter prompt

Turn this analytics request into a scoped analysis: [paste request or link to source context]. Identify the business question, required metric definitions, source exports, relevant dashboards, and recent product or business context. Draft an analysis plan, run a first-pass analysis using available data, validate the outputs, and prepare a stakeholder-ready answer with charts, caveats, source links, and open questions for analyst review. Do not assume definitions or join logic that are not provided.

##### Real-world example

Turn the enterprise trial conversion request into a scoped analysis. Use the original request thread, metric definition, source-of-truth table exports, related…