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49 open Data Scientist jobs

Real, currently-open Data Scientist roles synced directly from company career pages. Upload your resume to see which of these match your skills, or browse all open roles. New to the role? Read our Data Scientist guide.

Hiring snapshot for Data Scientist

The most common locations right now are Remote - United States (6), New York, NY; Pittsburgh, PA (4), San Francisco, CA, US; Remote, US (3). 16 of these 49 roles (33%) list a remote option. 37 were posted in the last two weeks. By team, the biggest groups are Data & AI (49).

Computed from the 49 open listings shown below.

Guide: working as a Data Scientist

Data scientists use statistics, experiments and models to answer questions that shape product and business decisions. A typical week mixes framing an ambiguous question, pulling and cleaning data, exploring it, testing hypotheses or building a predictive model, and explaining the result to non-technical stakeholders in plain language.

Core skills employers look for

  • Statistics and hypothesis testing
  • Python with pandas and NumPy
  • SQL for exploratory analysis
  • A/B test design and analysis
  • Regression and classification
  • Feature engineering
  • Exploratory data visualisation
  • Communicating uncertainty

How to stand out when you apply

  • Publish a notebook project that starts with a real question, states assumptions, shows exploratory analysis and ends with a recommendation, not just a model score.
  • Show statistical judgement: explain why you chose a metric, how you checked for leakage, and what a confidence interval actually means for the decision at hand.
  • Use a messy public dataset instead of a polished competition set, and document your cleaning decisions, since real work spends much of its time there.
  • Tailor projects to the domain you are applying to, such as churn for subscription products or demand forecasting for retail, so the relevance is obvious.

What the interviews usually cover

Rounds commonly include SQL queries, a statistics and probability discussion, a modelling case where you choose features and evaluation metrics, and a product question about measuring whether a feature worked. Some employers set a take-home analysis. Interviewers listen for how you handle bias, sample size and confounders, and whether you can explain results without jargon.

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