What should a data scientist resume include?

Contact links (GitHub matters), a short skills section, experience or projects with bullets that name the problem, method, data size and result, and education. Publications go in their own section if you have them.

How long should a data science resume be?

One page for most people. Two pages is fine for PhDs or people with a long publication or research record.

What are good data science projects for a resume?

Projects on real, messy data that answer a specific question, compare against a baseline, and end in a finding or a deployed app. Link the repo and summarise the result in one line.

Do I need a PhD on my data scientist resume?

No. Many data science roles hire from bachelor's and master's programs. Show applied results instead: experiments run, models shipped and decisions changed.

How to write a data scientist resume

By Mubeen Jawed, founder of ResumeForDev · Updated November 7, 2026

Short answer

A data scientist resume should show models and analyses that changed a product or business decision, not just the algorithms you know. Each bullet should name the problem, the method, the data size and the result, such as “Built a gradient-boosted churn model (XGBoost, 2M users) that lifted retention campaign conversion 11%.” List Python, SQL, your ML libraries and statistics methods, link one or two strong projects, and keep it to one page unless you have a research record.

Key points

Lead with impact: the decision or metric your model moved.
Name the method and the data scale in the bullet.
Python, SQL and statistics are the baseline; list libraries you actually used.
Projects should end in a finding or a deployed model, not just accuracy on a test set.

What data science hiring managers look for

Data science roles range from analytics-heavy (experiments, metrics, SQL) to engineering-heavy (training and shipping models). Read the post and lean your resume toward the side it describes. Either way, reviewers look for the same core: you framed a real problem, chose a sensible method, and got a result someone used.

Before and after

Before:Developed machine learning models to predict customer churn.

After:Built an XGBoost churn model on 2M customer records (AUC 0.84) that the retention team used to target offers, lifting campaign conversion 11% over the rules-based baseline.

Model quality alone (AUC) is half the story. The business result is the other half.

Example data scientist bullets

Designed and analysed 30+ A/B tests on pricing and onboarding, building a CUPED variance-reduction step that shortened average test length from 4 weeks to 2.5.
Built a demand-forecasting model (LightGBM, 600 SKUs) that cut forecast error from 22% to 13% and reduced overstock costs.
Trained a text classifier on 150k support tickets (fine-tuned BERT) to route tickets automatically, cutting first-response time by 35%.
Defined the company's north-star engagement metric with product leadership and built the SQL and dbt models behind it.
Shipped a recommendation model behind a FastAPI service with daily retraining in Airflow, serving 1.5M users.

Data science resume skills

Languages: Python, SQL, R
ML and stats: scikit-learn, XGBoost, PyTorch or TensorFlow, statsmodels, A/B testing, causal inference
Data: pandas, Spark, dbt, Snowflake or BigQuery
Deployment: FastAPI, Docker, MLflow, Airflow, AWS SageMaker
Communication: experiment readouts, dashboards (Tableau, Looker)

Only list methods you can explain in an interview. A data science interviewer will pick one line from your skills and ask you to derive or defend it. For keyword lists across data roles, see resume keywords by role.

Projects on a data science resume

For students and career changers, projects carry the resume. The ones that stand out use messy real data, compare against a simple baseline, and say what the result means. “Predicted house prices with 92% accuracy” on a tutorial dataset says little. “Scraped 40k rental listings and showed the 3 features that drive price in my city, with a deployed Streamlit app” says a lot. See how to put projects on a resume.

On ResumeForDev, set your target role to “Data Scientist”. Without a job post, the keyword check compares your resume against a data and ML vocabulary. Paste a job description and the keyword check uses that post's terms instead; on Pro, the writer also tailors the bullets to it. Before writing, it asks follow-up questions about the numbers above, and it removes any number or skill you never gave it. Your first resume is free.

Frequently asked questions

What should a data scientist resume include?: Contact links (GitHub matters), a short skills section, experience or projects with bullets that name the problem, method, data size and result, and education. Publications go in their own section if you have them.
How long should a data science resume be?: One page for most people. Two pages is fine for PhDs or people with a long publication or research record.
What are good data science projects for a resume?: Projects on real, messy data that answer a specific question, compare against a baseline, and end in a finding or a deployed app. Link the repo and summarise the result in one line.
Do I need a PhD on my data scientist resume?: No. Many data science roles hire from bachelor's and master's programs. Show applied results instead: experiments run, models shipped and decisions changed.