How do you hire a data scientist for a technology company?

In short

Hire a data scientist who can take a business question to a validated model and explain the result to the people who decide. O*NET's core tasks for Data Scientists run from cleaning raw data and processing large datasets to comparing models on statistical metrics and presenting findings to management, so test each of those steps rather than tool names.

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What does a data scientist do at a technology company?

A data scientist at a technology company turns product and business data into decisions. O*NET records the role as Data Scientists (O*NET 15-2051.00), and its core tasks include:

  • Analyze, manipulate, or process large sets of data using statistical software.
  • Clean and manipulate raw data using statistical software.
  • Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.
  • Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
  • Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.
  • Identify business problems or management objectives that can be addressed through data analysis.

Which skills should a data scientist job description test for?

A data scientist job description should test the whole path from messy data to a decision: cleaning, modelling, validating and explaining. A candidate who can only fit models, or only build dashboards, covers part of the job.

O*NET's technology list for Data Scientists includes Apache Spark, Apache Airflow, Apache Kafka, Apache Hadoop, Apache Hive and Amazon Redshift. Ask for the data platform your company runs; strong statistics transfers across platforms faster than platform knowledge transfers into statistics.

  • Cleaned raw data and documented what was removed and why
  • Compared candidate models on a stated metric, such as a loss function or explained variance
  • Validated a model against outcomes before it was used
  • Presented a result to management that changed a decision

How much of a data scientist's work do people bring to AI tools?

0.61% of sampled Claude.ai conversations in May 2026 were about the tasks of the O*NET occupation behind this role, according to the Anthropic Economic Index. In those conversations, 47.43% asked Claude to complete the task directly (automation) and 52.57% worked with it iteratively (augmentation).

Data Scientists is one of the occupations where augmentation outweighed automation in that sample. For hiring, ask how the candidate checks analysis or code an assistant produced, and which steps they keep for themselves.

How do you test a data scientist before an offer?

Test a data scientist with a small take-home or live exercise on a real, messy dataset: ask for a cleaned dataset, a model compared against a baseline, and a short written recommendation for a manager. The exercise covers the core tasks in one sitting.

Common questions

Is a data scientist the same as a data analyst?
A data scientist builds and validates predictive models, while a data analyst mainly queries data and reports on patterns and trends. O*NET files each role under its own occupation.
Does a data scientist need big data tools?
Big data tools matter only when your data needs them. O*NET lists Apache Spark and Apache Hadoop for Data Scientists, but a team on a single warehouse should test statistics and modelling first.

Where this data comes from

  • O*NET 31.0 DatabaseThis page includes information from the O*NET 31.0 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA. EmployLabs has modified all or some of this information. USDOL/ETA has not approved, endorsed, or tested these modifications.
  • Anthropic Economic Index, release_2026_06_26 (claude_ai, global, soc_occupation, May 2026)Anthropic Economic Index (Anthropic), data released under CC BY. Usage share is the share of sampled Claude.ai conversations classified to this occupation; the automation/augmentation split describes how those conversations used Claude. Neither measures the labour market.

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