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Data scientist resumes · Word & Google Docs

Free Data Scientist Resume Templates

Free, ATS-friendly data scientist resume templates in Microsoft Word and Google Docs — genuinely free, no account and no paywall. Each one leads with what data-science hiring screens for: a keyword-rich technical stack (Python/R, SQL, PyTorch, Spark, AWS, MLOps) and quantified impact — model performance, business lift, and models in production. Three designs for three lanes — Senior Data Scientist, ML Engineering, and Applied / Analytics — each in three colors. Pick the one that matches your role and drop in your stack.

The templates

Three data scientist designs, each in three colors.

Senior Data Scientist for end-to-end, in-production scientists (a model-metrics dashboard and tech stack); ML Engineering for production/MLOps-leaning roles (a Data → Features → Train → Deploy → Monitor pipeline flow); and Applied / Analytics for product and experimentation scientists (A/B testing, causal inference, and business impact). Every one opens in Word or Google Docs, stays on one page, and is built to be ATS-friendly — stack and metrics front and center.

Senior Data Scientist — Blue
A senior layout with a full-bleed dark header and a model-metrics dashboard (AUC, revenue impact, models in production) over a labeled tech-stack block — Python/R, PyTorch, Spark, AWS SageMaker, NLP. Built for end-to-end, in-production data scientists.
Senior Data Scientist — Indigo
The senior design in indigo — a metrics-forward header and keyword-rich technical stack for staff and senior data scientists who ship deep-learning models and prove business lift.
Senior Data Scientist — Teal
The senior layout in teal — a model-metrics dashboard and tech-stack block for experienced data scientists leading the full ML lifecycle from research to production.
ML Engineering — Teal
A production-ML layout with a pipeline flow (Data → Features → Train → Deploy → Monitor) and an MLOps tech stack — Kubernetes, MLflow, Airflow, Docker. For data scientists who ship and own models in production.
ML Engineering — Blue
The ML-engineering design in blue — a lifecycle flow and MLOps tooling for machine-learning engineers and DS who live where modeling meets infrastructure.
ML Engineering — Slate
The ML-engineering layout in slate — a production-pipeline format foregrounding feature stores, CI/CD, drift monitoring, and Kubernetes for ML engineers.
Applied / Analytics — Violet
An applied-science layout with a full-height sidebar foregrounding A/B testing, causal inference, and business impact — experiments run, conversion lift, Tableau / dbt / Looker. For product and analytics data scientists.
Applied / Analytics — Blue
The applied design in blue — a sidebar layout for experimentation- and product-analytics data scientists who turn A/B tests and causal studies into business decisions.
Applied / Analytics — Teal
The applied / analytics layout in teal — a credential sidebar built around experimentation, statistical modeling, and product impact with Tableau, dbt, and Looker.
What to include

What goes on a data scientist resume.

Data-science hiring managers and the ATS both scan for your toolkit and your impact. Put them where they can't be missed — which is what these templates do:

  • A labeled technical stack. Languages (Python, R, SQL), ML/DL (PyTorch, TensorFlow, scikit-learn, XGBoost), big data (Spark, Kafka), and cloud/MLOps (AWS SageMaker, Docker, MLflow) — spelled out.
  • Quantified model and business impact. AUC/precision, revenue or cost impact, conversion lift, experiments run, and models shipped to production — numbers beat duties.
  • The right lane. Research-to-production DS, ML-engineering/MLOps, or applied/experimentation — lead with the methods that match the job (deep learning, pipelines, or causal inference and A/B testing).
  • New grad? Projects and Kaggle. Feature your thesis, capstone, competitions, and internships as experience, each with the stack and the result.

Adjacent role? For BI and reporting-focused work see data analyst templates, and for software and platform roles, the software engineer designs.

Make it yours

Fill it in and apply.

  1. Click Open in Google Docs to copy it into your Drive, or Download Word for the .docx.
  2. Fill the technical stackblock with your real tools — match the posting's keywords (Python, SQL, PyTorch, Spark, MLflow) for the ATS.
  3. Rewrite each bullet around a metric and an outcome — model performance, revenue or conversion lift, models in production, experiments run.
  4. Export a PDF to send and keep a Word copy for company application portals.
Common questions

Data scientist resume FAQ

What should a data scientist resume include?
A keyword-rich technical stack (Python/R, SQL, ML/DL frameworks like PyTorch and scikit-learn, big-data tools like Spark, and cloud/MLOps), plus quantified impact — model performance (AUC, precision), business lift (revenue, conversion), and models shipped to production. Lead with the stack and the numbers; both the hiring manager and the ATS screen for them.
How do I show machine-learning projects and impact?
Quantify everything. Pair the model with a metric and an outcome — "fraud model (PyTorch) at AUC 0.94, cutting losses ~$24M/yr," "causal study drove +19% conversion," "12 models in production." The Senior design has a model-metrics dashboard and the Applied design a stats strip built for exactly this.
Data scientist vs. ML engineer vs. applied scientist — which template?
Use Senior Data Scientist for end-to-end DS roles (research → production, metrics-forward); ML Engineering for production/MLOps-leaning roles (pipelines, deployment, monitoring); and Applied / Analytics for product and experimentation roles (A/B testing, causal inference, business impact). All three are keyword-tuned for their lane.
Are these data scientist resume templates ATS-friendly?
Yes — they spell out tools and methods (Python, SQL, PyTorch, Spark, MLflow, A/B testing, NLP) in clean, parseable layouts with standard headings rather than burying them in graphics, so they read correctly in Word, Google Docs, and applicant tracking systems.
How do I write an entry-level or new-grad data scientist resume?
Lead with education and a strong projects/skills section — your thesis, Kaggle competitions, capstone, and internships count as experience. Show the stack you used and the result of each project (metric or finding). Put projects where work experience normally goes until you have industry roles.
Should I include a separate skills / tech-stack section?
Yes. A labeled tech-stack block (Languages, ML/DL, Big Data, Cloud/MLOps, Specialties) is the fastest way to match the job posting's keywords for the ATS and let a reviewer scan your toolkit in seconds. All three designs include one.
Can I edit the template in Word and Google Docs?
Both. Click Download Word for the .docx, or Open in Google Docs to make your own copy in Drive. Everything is free — no account, no paywall.
How long should a data scientist resume be?
One page for most candidates, including early-career and most mid-level scientists; principal/staff scientists with significant publications or patents can justify two. These designs are built to hold cleanly on one page.

Data scientist resume templates · Updated June 2026

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