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Data Scientist: AI job impact

AI can draft analysis code, cleaning steps and charts. Statistical design, leakage checks and the interpretation of results require independent review.

U.S. occupation dataData checked October 25, 2026Methodology
Data Scientist at work

AI applicability

0.357 / 1

How applicable AI was to this occupation’s work in Microsoft’s study. This is an index, not a probability.

Observed activity coverage

77.2%

Share of work activities above the study’s usage threshold, weighted by importance. Not the share of workers using AI.

Direct occupation matchData Scientists · 15-2051.00

The published SOC score covers the broader Data Scientists occupation, including O*NET specialties.

Microsoft v1.1 · U.S. Bing Copilot conversations, Jan–Sep 2024. These indicators do not predict job losses and do not change with your task selections. Measurement details

Your daily tasks

Set how often you do each task to update your task mix, plan and tool suggestions.

8 of 8 tasks selectedFrequency-weighted task mix
Partial automation
25.0%2 tasks
AI assistance
37.5%3 tasks
Human-led
37.5%3 tasks

Frequency weights: occasionally 1, regularly 2, often 3. These shares do not represent working hours, time saved or job-loss risk.

AI roles are AI IDE List editorial assessments of O*NET tasks, not measured automation rates. Partial automation requires review.

Write data analysis code

Draft a reproducible analysis; inspect row counts, missing values and assumptions at each transformation.

Task source

O*NET original task statement (English)

Analyze, manipulate, or process large sets of data using statistical software.

O*NET 31.0, task 21823 · Importance 4.33 / 5. View occupation tasks

AI roleAI assistance

Select model features

Suggest candidate features; test leakage, stability and whether each input is available at prediction time.

Task source

O*NET original task statement (English)

Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.

O*NET 31.0, task 21824 · Importance 3.75 / 5. View occupation tasks

AI roleAI assistance

Design sampling methods

Compare sampling options; choose the population and assess selection bias before collecting data.

Task source

O*NET original task statement (English)

Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.

O*NET 31.0, task 21825 · Importance 3.00 / 5. View occupation tasks

AI roleHuman-led

Clean raw data

Generate cleaning rules from a schema; review rejected rows and preserve an unchanged input copy.

Task source

O*NET original task statement (English)

Clean and manipulate raw data using statistical software.

O*NET 31.0, task 21826 · Importance 4.04 / 5. View occupation tasks

AI rolePartial automation

Compare model performance

Draft evaluation code; verify splits, baselines and metrics on independent data.

Task source

O*NET original task statement (English)

Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.

O*NET 31.0, task 21827 · Importance 4.04 / 5. View occupation tasks

AI roleAI assistance

Create charts

Generate a chart specification; verify axes, units, denominators and the underlying numbers.

Task source

O*NET original task statement (English)

Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.

O*NET 31.0, task 21828 · Importance 4.33 / 5. View occupation tasks

AI rolePartial automation

Present analytical results

Draft an explanation of measured results; state uncertainty and distinguish association from causation.

Task source

O*NET original task statement (English)

Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.

O*NET 31.0, task 21829 · Importance 4.21 / 5. View occupation tasks

AI roleHuman-led

Define the business problem

Explore possible analytical questions; agree the decision and success criterion with the owner.

Task source

O*NET original task statement (English)

Identify business problems or management objectives that can be addressed through data analysis.

O*NET 31.0, task 21831 · Importance 4.13 / 5. View occupation tasks

AI roleHuman-led

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Your AI adaptation plan

Start with one recurring task. Test the output before expanding the workflow.

  1. Validate AI-assisted analysis

    For: Write data analysis code

    Prepare
    Use a sanitized sample with a data dictionary, units and a clear question.
    Try
    Draft a reproducible analysis; inspect row counts, missing values and assumptions at each transformation.
    Verify
    Recompute a small sample independently and check joins, missingness and denominators.

  2. Check whether tests detect failures

    For: Compare model performance

    Prepare
    Use a requirement and a known failing example, without showing the proposed implementation first.
    Try
    Draft evaluation code; verify splits, baselines and metrics on independent data.
    Verify
    Confirm the tests fail on the broken example and pass on the corrected behavior.

  3. Check evidence and uncertainty

    For: Design sampling methods

    Prepare
    Define the question and provide primary sources with publication dates.
    Try
    Compare sampling options; choose the population and assess selection bias before collecting data.
    Verify
    Open original sources, check scope and separate findings from assumptions.

Design reliable evaluations

Build a simple baseline and a held-out evaluation. Audit leakage and subgroup errors before comparing a more complex model.

Skills and career outlook

Skills in the occupation data

Importance · 1–5
  • Mathematics4.38 / 5
  • Critical Thinking4.25 / 5
  • Reading Comprehension3.88 / 5
  • Complex Problem Solving3.75 / 5
  • Programming3.75 / 5

Top O*NET essential and transferable skills by importance. Ratings describe occupational requirements, not future demand.

U.S. employment projection

+34.6%

Projected employment change, 2025–2035

Data scientists

BLS projects total employment change from many factors. This is not an AI-specific forecast or a UK projection.

BLS employment projections

What to keep evaluating

A convincing explanation does not establish causality. Leakage, biased samples and invalid evaluation splits can make a model look better than it is.

Tools to try with your tasks

Matched to the tasks you selected. Test on a small example and compare output quality and review effort.

Claude Opus 5.5

Model

A model to evaluate on a small, verified example of this task. Compare quality and review effort before adopting it.

Try for: Write data analysis code; Select model features.

Cursor

Tool

Draft and review small code, SQL and test changes in your project.

Try for: Write data analysis code; Select model features.

GPT-6 Astra

Model

A model to evaluate on a small, verified example of this task. Compare quality and review effort before adopting it.

Try for: Write data analysis code; Select model features.

Claude Code

Tool

Explore a codebase and propose a bounded change with tests.

Try for: Compare model performance.

Claude Code commands
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Sources and limits

Matched occupation: Data Scientists (15-2051.00). Published research group: Data Scientists (SOC 15-2051).

Microsoft’s study uses O*NET 29.0; the task descriptions here use O*NET 31.0. The original study score is preserved. Editorial suggestions are not results reported by these sources.

Read the methodology, attribution and update record