Data Scientist CV Example & Writing Guide

The full CV example

Read it as a shape, not a script. The numbers below belong to an invented person and none of them will be yours.

Fictional example. Use your own name, city, email, phone and one profile link.

Why this CV works

Five things this example does that a modelling CV usually does not:

• The headline names problems, not a library list

  "Forecasting, pricing and claims models" tells an insurance hiring manager they are in the right place and tells a computer vision team they are not. "Data Scientist - Python, ML, AI" says only that the file was written by a data scientist, which the job title already covered.

• One result is a recommendation not to ship

  The pricing nudge got tested and rejected: 0.4% lift, interval crossing zero, six weeks of engineering saved. Most data scientist CVs contain no negative results at all, which is odd for a job that exists to tell people when the evidence is thin. An interviewer who sees that bullet will ask how the decision was framed, and that is a conversation this candidate wants.

• Every model is attached to somebody who used it

  The triage model routes claims. The renewal list is worked by the retention team. The delay forecast is what the depots staff against. A model with no named user is a model that may never have left a notebook, and a reader who has funded data teams before knows it.

• The metric bug is on the CV

  Three points of overstated on-time delivery, found and corrected, with the SLA report restated. Putting the discovery of an embarrassing error on your own CV is a strong signal, because the alternative was quietly fixing it and telling nobody.

• Scope is admitted before anyone has to ask

  "Data team of 9 sitting next to claims" stops a reader from imagining a platform owner, and stops an interviewer catching the inflation later. Adding the team size costs seven words and removes a whole category of awkward follow-up.

Professional summary examples

Entry level

With one year of work, name the thing that is actually running and who runs it. Do not describe a course project in language that implies production.

Mid level

Mid-level is where one checkable number belongs. Two sentences of evidence, one sentence of scope.

Senior

Senior summaries say what you are trusted to decide. The last sentence is an offer, and it is a sharper offer than a year count.

Experience bullet examples

Twelve bullets in weak and strong form. The strong versions are not better because they are longer. They are better because a reader can disagree with them, and because the figures in them are the writer's own. Yours have to be too.

Weak

Built machine learning models for the business.

Stronger

Rebuilt the claims triage model routing 4,000 claims a week, raising fast-track precision from 0.61 to 0.83 and removing about 1,100 manual reviews a month.

Why

The weak version is true of every data scientist alive. The strong one names the volume, the metric and the operational consequence.

Weak

Improved model accuracy.

Stronger

Raised recall on fraud referrals from 0.44 to 0.67 while holding the false-positive budget the investigations team had agreed to.

Why

Accuracy on its own is meaningless without the trade-off you were holding fixed. The constraint is the interesting half.

Weak

Worked with stakeholders to understand requirements.

Stronger

Sat with the retention team for two afternoons before writing any code, which is how the churn model became a weekly list of 300 accounts instead of a dashboard.

Why

Say what the conversation changed. Otherwise this is a sentence about attending meetings.

Weak

Ran A/B tests.

Stronger

Tested a pricing nudge on 60,000 quotes and recommended against shipping it: 0.4% lift with an interval crossing zero, against six weeks of engineering.

Why

A negative result you defended is worth more than three positive ones you cannot describe the design of.

Weak

Used SQL and Python daily.

Stronger

Rewrote the depot scorecard query from a 400-line view into six dbt models, which cut the nightly build from 50 minutes to 9 and made the metric definitions reviewable.

Why

Languages belong inside an outcome. Two numbers and a reason the outcome mattered.

Weak

Deployed models to production.

Stronger

Took the triage model from notebook to a scheduled service with monitoring and a documented rollback, and ran it through the first two retrains myself.

Why

"Deployed" hides whether you handed it over or lived with it. Living with it is the harder claim and the more valuable one.

Weak

Created dashboards and reports.

Stronger

Cut the weekly operations report from 40 slides to six charts, after asking the four readers which numbers they had ever acted on.

Why

The method is the point here. Deleting things nobody uses is a skill people will pay for.

Weak

Ensured data quality.

Stronger

Found a duplicate-scan bug that had overstated on-time delivery by three points for two years, and restated the customer SLA report with the corrected definition.

Why

Naming the size of the error and what you restated turns a chore into a story an interviewer will ask about.

Weak

Worked with big data technologies.

Stronger

Moved four notebooks onto Airflow and dbt, which ended a Monday rerun that had cost an analyst two hours a week and produced a different number each time.

Why

Nobody is buying the technology. They are buying the end of the Monday rerun.

Weak

Monitored models after deployment.

Stronger

Wrote model cards and drift monitoring for every production model; drift on the triage model was caught five weeks before it would have appeared in the loss ratio.

Why

The gap between detection and consequence is the number that proves monitoring was worth the effort.

Weak

Presented findings to management.

Stronger

Presented the renewal-risk work to the board in four slides with one recommendation, and the retention team had the list the following Monday.

Why

How fast the recommendation turned into an action is the measure of the presentation, not how many slides it had.

Weak

Mentored junior team members.

Stronger

Reviewed the analysis of two junior analysts weekly for a year; both now run their own experiments and one owns the pricing report end to end.

Why

Mentoring claims need an outcome you did not control, or they read as self-assessment.

Skills that belong on this CV

Education and certifications

Data science is one of the few fields where a quantitative degree still gets read at the third role, because the reader wants to know whether you were taught to doubt a result. Keep it to degree, institution, years and one line on the thesis if it is relevant. A PhD belongs above experience only while the publications are the strongest evidence you have; once you have shipped models that other people depend on, it moves down. Career changers from a bootcamp should name the programme in one line and spend the recovered space on two projects with real users.

Certifications

• Google Professional Machine Learning Engineer or AWS Certified Machine Learning - Worth a line when the advert names the cloud, because the platform team will ask. Next to shipped models it adds little.
• Deep Learning or causal inference course certificates - These are coursework, and a reader who has hired before knows it. List at most one, and only if it covers a gap the rest of the CV leaves open.

There is no certification a data scientist needs. A public notebook, a package other people install, or a written analysis of a problem you chose yourself does more work than any of them.

How an ATS reads this CV

Appliora's ATS Checker runs 14 checks against your uploaded file and shows you the text its parser pulled out. Four of them catch what goes wrong on modelling CVs specifically.

• Reading order

  Data CVs attract two-column templates with the stack in a narrow sidebar, and sometimes a small chart as decoration. When a parser reads across the page rather than down the column, the sidebar tokens land interleaved with your job titles and the result is unreadable to a human too. The checker shows the extracted text in reading order, so you can see whether this happened to your file rather than guessing.

• Concrete skills

  A block of forty comma-separated tokens scores worse than twelve, not better. The check looks for recognisable skills rather than orphan fragments, and "ML", "AI", "NLP" and a bare version number read as noise. Write "scikit-learn" rather than "sklearn" and let the model families appear inside the bullets where they have a problem attached.

• Bullet quality

  This is the heaviest content check. It looks at how many of your bullets carry a figure and how many open with an action verb. Modelling CVs tend to pass the figure test and fail the verb test, because a project description that starts "Model to predict..." reads as a title rather than as work someone did.

• Format traps

  Skill rating bars, icon fonts for contact details, and screenshots of plots are all invisible to a parser, and a chart pasted as an image takes space that could hold a sentence about what the chart changed. Keep the evidence in text and link a notebook if you want the visual seen.

Templates that suit this role

• ATS Structured

  Single column, conventional headings, no sidebar. The safest choice when the application goes through a large insurer's or bank's portal.

• Tech Stack

  Gives the stack a real block without squeezing it into a narrow column, so the tooling is visible to a human without becoming a parsing risk.

• Academic / Research

  Built for a publication and thesis list, which is what a recent PhD needs on page one before the industry projects arrive.

Related roles

• Data Analyst
• Business Analyst
• Product Manager
• Software Engineer