Data Scientist CV Example & Template
A complete two-page CV for a data scientist with an MSc, four years of applied modelling in UK fintech and retail, and models in production. It is followed by what hiring managers check first. Use the structure, replace the models, metrics and tools with your own, and report results against a baseline wherever you can.
Last reviewed October 2026
Example Data Scientist CV
Aisha Rahman
Data Scientist, forecasting and risk modelling
London, UK
Profile
Data scientist with four years of building and shipping models for a lender and a grocery retailer. I work in Python and SQL, deploy models behind APIs, and measure them against what the business used before. Strongest in forecasting, classification on imbalanced data and experiment design. Looking for a senior data scientist role on a team that owns its models in production.
Experience
Data Scientist
Jun 2023 – Present
Lindenhall Finance · London
- Rebuilt the early-arrears model as gradient boosted trees; recall on accounts that went 60 days late rose from 54% to 71% at the same false-positive rate.
- Deployed the model as a containerised API on AWS, scoring 1.2 million accounts a night, with drift monitoring that alerted the team twice in the first year.
- Ran a six-week champion-challenger test on collections messaging across 80,000 customers; the winning variant lifted promise-to-pay rates by 3.1 points.
- Mentor two junior analysts and review all model code in the team for tests, leakage and reproducibility before it goes to production.
Junior Data Scientist
Sep 2021 – May 2023
Harvest Lane Grocers · London
- Built a demand forecast for 600 fresh-food lines per store using LightGBM with promotions and weather features; weekly error fell 14% against the previous method.
- Cut stock waste on bakery lines by an estimated £410,000 a year after the forecast was adopted in 120 stores.
- Wrote a feature pipeline in Python and SQL on BigQuery that replaced six manual spreadsheets, and documented it for the next hire.
- Presented model results to category managers each month, translating forecast error into units and pounds.
Education
MSc Statistics and Machine Learning, Distinction
2020 – 2021
Imperial College London
Thesis: calibrated probability estimates for rare-event classification.
BSc (Hons) Mathematics, First
2017 – 2020
University of Warwick
Skills
Python (scikit-learn, LightGBM, PyTorch) · SQL and BigQuery · Time series forecasting · Classification on imbalanced data · A/B testing and causal inference · Model deployment with Docker and AWS · MLflow and model monitoring · Feature engineering · Statistical modelling · Git and code review · Communicating results to non-technical teams
Publications and talks
- Calibration for rare-event classifiers, MSc thesis, Imperial College London, 2021
Why this Data Scientist CV works: what shortlisters check first
Models in production, not only notebooks
Hiring managers screen hard for evidence that you have put a model into use. Say what the model predicted, how it was deployed, how often it runs and who relies on it. A deployed model with monitoring tells them far more than a list of algorithms. If you have not shipped, describe an end-to-end project honestly, including where it stopped.
Results against a baseline
A bare accuracy figure means little. Say what you beat: the previous model, a simple rule, or the business average. Pick the metric the use case needs, such as recall at a set false-positive rate, forecast error, uplift or revenue, and state the period. Interviewers often ask how you chose the metric, so be ready to explain.
Statistics and experiment design
Beyond machine learning, teams want people who can design a test, size it, and read it without fooling themselves. Mention A/B tests, causal methods, calibration or uncertainty work you did, with sample sizes. Many employers set a statistics or case-study round, and a CV that names these skills sets you up for it.
The stack in the advert
Name your languages, libraries, cloud and orchestration tools, for example Python, SQL, scikit-learn, PyTorch, AWS, Docker, Airflow or MLflow, in the same words as the posting. Recruiters filter on them. Lead with what you used most recently, and drop tools you touched once.
Communication and business sense
A data scientist spends much of the week explaining trade-offs to people who do not code. Show it: presenting to managers, writing documents that changed a decision, agreeing a metric with the business before building. A bullet on mentoring or code review also shows you can work in a team.
Key skills recruiters look for
How to write each section of a Data Scientist CV
Profile
Name your specialism, the domain you know, the tools you use and one proof point, in three or four sentences. 'Forecasting and risk modelling in financial services' is more useful than 'passionate about data'. State whether you want to move towards research, applied work or leadership.
Experience
Give each model the same shape: the problem, the method, how you deployed it and the measured result against a baseline. Two or three of these per job is enough. Add one bullet on teamwork, such as code review or mentoring, and one on how you communicated results.
Education
Give the degree, university, classification and a thesis title if it is relevant. For a PhD, add one line on the problem and methods, and any publications. Do not list online course certificates unless they are recent and close to the job.
Skills
Group them: languages, modelling methods, data and cloud tools, and engineering practice. Put the advert's keywords first. Only list a method such as deep learning or NLP if you can describe a project where you applied it.
Projects and publications
Keep this short: one or two projects or papers that match the role. Say what question you asked and what you found. If you contribute to open source, name the project and what you changed. Recruiters rarely open more than one link.
More example bullet points
Strong Data Scientist CVs lead with measurable impact. Here are examples of the kind of bullets that make recruiters stop scrolling:
Built customer churn prediction model with 89% accuracy, enabling proactive retention campaign that saved $2.4M ARR
Designed A/B testing framework adopted company-wide, running 50+ experiments per quarter with rigorous statistical analysis
Developed NLP pipeline for sentiment analysis processing 100K+ customer reviews, informing product roadmap priorities
Common Data Scientist CV mistakes
- Listing 20 algorithms with no model that shipped or no result against a baseline.
- Quoting accuracy on an imbalanced problem, where it hides poor performance.
- Leaving out how the model was deployed and who used it.
- Describing a standard tutorial project, such as digit recognition, as proof of production work.
- Writing 'AI expert' or 'machine learning guru' with no model, dataset or metric to back it up.
Full guide
Data Scientist CV guide: what recruiters actually look for →
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How to tailor your Data Scientist CV for each application
Every Data Scientist job posting is different. The skills they prioritize, the seniority they expect, the setting — all of it shapes what your CV should emphasize. A CV that gets an interview at one employer can miss at another, even for the same job title.
The fastest way to tailor your CV is to paste the job description into NextCV. The AI reads the posting, maps it to your experience, and rewrites your bullets to match what the employer is scanning for. 1 free preview, no signup required — you only pay when you want a clean PDF download. Sign up free for 3 more.
Data Scientist CV FAQ
Do I need a PhD to be a data scientist?
No. Most applied data science roles in the UK ask for a degree in a quantitative subject, and many hire at MSc or BSc level with project evidence. A PhD helps for research-heavy posts. For all roles, models you built and shipped carry more weight than the degree level.
How long should a data scientist CV be?
Two pages is standard for industry roles. One page works under two years of experience. Academic-style CVs with long publication lists can run longer, but for company jobs keep publications to the ones that match the role.
How do I show data science impact on a CV?
State the problem, the method and the result against a baseline, with a metric the business cares about and the period. For example, a recall gain at a fixed false-positive rate, a forecast error cut, or revenue added. Avoid accuracy alone on imbalanced data.
Should I include Kaggle or personal projects?
Yes, if you have little work experience, and keep it to one or two relevant projects. Pick questions with a real purpose, describe your method and result, and be ready to defend each choice. Tutorial datasets and copied notebooks do not help.
What is the difference between a data analyst and a data scientist CV?
An analyst CV leads with SQL, dashboards and decisions informed by analysis. A data scientist CV leads with modelling, experiments and deployed models. Both need clear results, but the scientist version shows methods, baselines and how models run in production.