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Data Scientist resume example

Models what is happening and what will happen next. Below is a complete data scientist resume, written the way one has to be written to survive a first screen, with every bullet explained underneath.

481
open right now
188
companies hiring right now
36%
of those openings are remote
Mostly in

San Francisco, London, New York

Hiring now
  • Reddit19
  • OpenAI16
  • Guidehouse15
  • Klarna14
  • Pinterest14
  • Airwallex13
  • Fetcherr10
  • Shift Technology10

Counted by Refolk from the public job boards of 1,745 companies, refreshed through the day. These are live openings, not a forecast, so the numbers move as roles are posted and filled.

Alex Moreno

Data Scientist

alex.moreno@example.com · +1 555 0134 · Berlin · alexmoreno.example

Summary

Data Scientist with eight years of experience. Built the churn model that identified at-risk accounts three weeks earlier, lifting retained revenue 6%. Looking for a data scientist role with more ownership of Python and the decisions around it.

Experience

Senior Data Scientist, Northwind Systems

2022 - Present

  • Built the churn model that identified at-risk accounts three weeks earlier, lifting retained revenue 6%.
  • Designed the experiment that killed a feature which looked positive in aggregate and was negative for new users.
  • Replaced a rules-based pricing engine with a model that lifted margin 3.4% while holding conversion flat.

Data Scientist, Meridian Labs

2018 - 2022

  • Owned the SQL side of the work at a company with a six-person data team, and documented it well enough to hand over cleanly.
  • Set the approach the team still uses for scikit-learn.

Skills

Python · SQL · scikit-learn · Statistics · A/B testing · Causal inference · Pandas

Education

BSc Statistics, 2018

A sample, not a real person. The name, the employers, and the figures are invented to show the shape of a strong page - use the structure and put your own evidence in it.

How to read this data scientist resume example

The sample above is not a template to copy word for word - copied resumes read as copied. It is here to show the shape of a page that gets past a first screen: one column, standard headings, and bullets that end in an outcome rather than a duty. Business metrics your models moved, with the evaluation to back it.

  • Every bullet opens with a verb and carries a number. That is the pattern, not a coincidence.
  • The summary makes a claim and then supports it in the first bullet underneath.
  • Skills are named tools, not adjectives, and each one appears again in the experience section.
  • The earlier role is short. Recent work carries the weight of the page.

The numbers in a data scientist resume

The most common thing missing from a data scientist resume is a number. Not because the work had none, but because nobody wrote them down at the time. These are the measures a data scientist can usually reach for, and the example uses them.

  • Built the churn model that identified at-risk accounts three weeks earlier, lifting retained revenue 6%.
  • Designed the experiment that killed a feature which looked positive in aggregate and was negative for new users.
  • Replaced a rules-based pricing engine with a model that lifted margin 3.4% while holding conversion flat.

Before and after: rewriting a weak bullet

Most data scientist resumes are one edit away from being much stronger, and the edit is the same every time: replace the description of the job with the result of doing it. The pairs below are the same work, written twice.

  • Weak: "Responsible for python and related tasks." Strong: "Built the churn model that identified at-risk accounts three weeks earlier, lifting retained revenue 6%."
  • Weak: "Worked on sql projects across the team." Strong: "Designed the experiment that killed a feature which looked positive in aggregate and was negative for new users."
  • Weak: "Helped improve processes and supported data scientist initiatives." Strong: "Replaced a rules-based pricing engine with a model that lifted margin 3.4% while holding conversion flat."

Adapting the example to your own history

Work backwards from the posting. Find the two or three things it actually screens for, then make sure the top third of your page answers them. Everything below that is supporting evidence.

  • Reorder your bullets so the one closest to the posting comes first in each role.
  • Rewrite the summary for the specific job. It is the only part a human reliably reads.
  • Cut any skill you would not want to be asked about for ten minutes.
  • Keep Python, SQL, scikit-learn visible in context, not stranded in a list.

FAQ

Can I copy this data scientist resume example?
Copy the structure, not the sentences. The layout, the ordering, and the shape of the bullets all transfer. The content does not: a recruiter who reads twenty resumes for the same role notices identical phrasing quickly, and the numbers in this sample are illustrative rather than anyone's real history.
What if I do not have numbers like these?
Most people have more than they think. Look for volume, frequency, time, cost, error rate, or a before-and-after on anything you touched. Where the number genuinely does not exist, say what changed and who noticed - "the process that used to need a weekly meeting now does not" is a real outcome without a metric.
How long should a data scientist resume be?
One page up to about eight years of experience, two pages beyond it. This example is one page. Length signals scope, so a long page with small-scope bullets reads worse than a short one.
Will this format pass an applicant tracking system?
Yes. One column, real text, standard headings, and no graphics or tables is the format parsers handle reliably. Most parsing failures come from multi-column layouts, text inside images, and headings the parser does not recognise.
Should the example change for a more senior data scientist role?
The structure stays. What changes is the scope in the bullets: more ambiguity you resolved yourself, more decisions with a tradeoff, and more effect on work that was not directly yours. Business metrics your models moved, with the evaluation to back it.

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Put this to work

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