Data science resumes have an inflation problem: every candidate "built models", so the phrase carries zero information, and screeners have adapted by ignoring it entirely. What hiring managers actually screen for is deployment and consequence: did the model ship, what decision or system consumed it, and what changed in a business metric you can name. The field also has a supply problem working against you: bootcamps and master's programs produce far more applicants than there are seats, so the first screen is fast and unforgiving, and the pages that survive are the ones where production evidence is visible in the top third rather than implied by a projects section.
The bullet pattern that works is strict: problem, method at its honest level of sophistication, and measured effect. "Built a churn model with XGBoost" is a class project sentence; "built the claims-triage classifier routing 60% of volume automatically at 98% precision, saving an estimated 11,000 adjuster hours a year" is a hire. Note what carries that sentence: the consumption (routing volume) and the consequence (hours), not the algorithm. A logistic regression that shipped beats a transformer that did not, and managers say so out loud. If your models never reached production, feature the analyses that changed decisions instead, and our bullet guide covers making that impact concrete.
Section order for a working data scientist: header, three-sentence summary, experience, skills split into modeling versus production, education last but never omitted, because DS is one of the few tech tracks where degrees still gate. New grads and academics flip it: education with thesis topic up top, then projects or research presented with the same outcome discipline as work bullets. Publications get their own short section only if venue-recognizable. What never works is the algorithm-zoo skills block (twenty model families, no evidence); it reads to any practitioner as a course syllabus, not a career.
Know the reading order. A recruiter goes first and matches on stack nouns: Python, the deployment platform, the method vocabulary in the req (causal inference, NLP, LLM), degree level, industry. The DS manager or lead reads second and hunts for the thing recruiters cannot check: whether you understand the gap between a notebook and a system. Words like monitoring, drift, retraining, evaluation harness, and champion/challenger carry more manager-weight than any algorithm name, because they signal you have lived with a model after launch day. Write the top third for the noun-matcher and the bullets for the skeptic.
Write the experience section as shipped systems and consumed decisions. Open each role with scope: the domain (claims, credit, pricing), the scale (models in production, transaction volume, users scored), the partners (actuarial, engineering, risk). Then make each bullet a consequence: hours saved, loss ratio protected, lift over control with the control named, incidents caught by monitoring you built. Claim your true role in team efforts precisely ("led migration", "partnered with engineering to ship"), because DS interviews probe exactly this seam. Use building verbs (shipped, deployed, designed, calibrated) over research verbs where true; our action verbs guide sorts them by claim strength.
Split skills into modeling and production, and mean both. The modeling line: Python with the libraries that matter (scikit-learn, XGBoost, PyTorch), the method families you can defend (causal inference, uplift, NLP, experiment design). The production line: SQL, the deployment stack (SageMaker, Vertex AI, MLflow, Docker, Airflow), feature stores, monitoring. That second line is where mid-level candidates separate from juniors, because "productionized" without tooling names fails both the recruiter filter and the manager sniff test. List nothing you cannot whiteboard: DS interviews are adversarial about depth, and one "I mostly used the defaults" on a listed specialty resets your level in the room. Grouping details in our skills guide.
Handle education by its real weight in this field. Degrees matter more in DS than in adjacent tech roles: an M.S. or Ph.D. belongs near your name (headline or summary) if you have one, and education stays two clean lines per degree otherwise. Thesis or dissertation topics earn a line only while you are within a few years of graduation or when directly relevant to the posting's domain. Certificates and MOOCs are floor-setters for career changers and near-invisible afterward; one line, late placement. If you are a self-taught exception with shipped production work, lead with the systems and let education sit quietly at the bottom; managers hire the evidence, and the degree filter softens exactly in proportion to how concrete your deployment bullets are.
Format for parsers, not for a poster session. One column, standard headings, common font, PDF export, no photo, no charts or model diagrams on the resume itself. The instinct to visualize is right for the portfolio and wrong for the resume: parsed text is what the first screen sees, and a two-column layout can shuffle your carefully ordered evidence into noise. Keep LaTeX for papers unless you are targeting research roles where its aesthetic is native. Name the file plainly: your name, the word resume. Full parsing rules and which layout features survive are in our resume format guide.
Avoid the recurring DS resume failures. The big five: algorithm lists with no shipped consequence; Kaggle placement presented as production experience (it is evidence of modeling skill, not of systems judgment, and managers read it exactly that way); precision/recall reported with no business translation; "leveraged AI" vagueness in the LLM era, which reads as prompt-only experience trying to sound like engineering; and the stale-notebook portfolio link with no README and no findings. Each is a minutes-level fix once seen. Run the draft against our common mistakes guide, and translate every model metric into the business number it protected or produced before you submit.
On the top third: the summary is three sentences with a fixed job to do: years and domains first, production scope second (models shipped, systems owned, scale scored), one consequence third. The level-calibrated variants below show the shape at entry, mid, and senior weight. If your history is research or an adjacent quantitative field, an objective naming the bridge beats a summary that strains for continuity; the objective examples cover the analytics-to-DS, academia-to-industry, and new-grad cases. Construction details in our summary guide.
Tailoring for DS postings is mostly method-vocabulary alignment. Reqs split into recognizable flavors (product DS with experimentation emphasis, ML-heavy builds, causal/econometrics seats, LLM application work), and the same history reads differently depending on which two bullets sit on top. Circle the posting's method nouns and mirror the ones you honestly own: "uplift modeling", "causal inference", "recommendation systems", "retrieval-augmented generation" are distinct filter strings, not synonyms. Match the deployment stack nouns the same way. Ten minutes per application, method in our tailoring guide.
The 2026 reality: LLMs restructured the field's middle. Routine modeling is increasingly automated or absorbed into platforms, while demand concentrated in two directions: evaluation and reliability engineering around LLM systems (harnesses, guardrails, cost/latency trade-offs, hybrid pipelines benchmarked against boring baselines) and rigorous causal and experimental work that models cannot fake. Resumes should meet that shift head-on: a bullet benchmarking an LLM extraction pipeline against a regex baseline and shipping the hybrid says more about your 2026 employability than any architecture name. Treat AI-assisted coding as assumed; treat AI judgment as the differentiator worth a bullet.
Use this page actively. The resume below is complete and realistic, rendered by the same engine that produces our PDF export; the "Use this example" button opens it in the builder so you can swap Kevin's history for yours instead of starting from a blank page. Then take structures from the bullet bank, check the keyword list against your target posting, and read the ATS extract at the bottom to see literally what a parser receives from this layout.