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https://careerdiary.io/assets/AtsCheckerDataScientistPage-CUAAYcCy.js

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1import{j as e}from"./animation-CddyXVCz.js";import{R as t}from"./RoleAtsCheckerPage-BPn8bsDA.js";import"./react-vendor-CbN3pvl2.js";import"./SEOLandingPageTemplate-B7gK1OY7.js";import"./index-DnWsV7cE.js";import"./supabase-iKSPld20.js";import"./docgen-Bp8WKBAE.js";import"./query-DPqpbKNH.js";import"./AtsScanTool-Cfl5ViOs.js";import"./Progress-D4MzP_N8.js";import"./ToolShareBlock-BQJTdbmx.js";import"./HandoffCard--ky8y7_b.js";import"./handoffSuggestions-raELPsOv.js";import"./GuestWatchCard-Bd47pYDe.js";const y=()=>e.jsx(t,{config:{role:"Data Scientist",rolePlural:"Data Scientists & Analysts",slug:"data-scientist",seo:{title:"Free ATS Resume Checker for Data Scientists & Analysts (2026) | CareerDiary",description:"Scan your data science resume against any job description. Instant ATS score, the ML/statistics/tooling keywords you're missing, and format fixes — free, no signup.",keywords:["ATS resume checker for data scientists","data scientist resume keywords","data analyst ATS resume","machine learning resume checker","ML engineer resume keywords"]},hero:{headline:"ATS Resume Checker for Data Scientists & Analysts",subheadline:`Data roles are the most keyword-dense field in hiring: one posting wants "machine learning", the next "predictive modeling", a third "MLOps" — and titles blur between Data Scientist, Data Analyst, ML Engineer, and Analytics Engineer. A recruiter's search can also miss you when you describe the same skill at the wrong altitude ("built models" vs the JD's "XGBoost"). Scan yours below against a real posting.`},keywordGroups:[{label:"Languages & querying",terms:["Python","R","SQL","PySpark","Scala"]},{label:"ML & statistics",terms:["machine learning","deep learning","natural language processing (NLP)","regression","classification","clustering","A/B testing","statistical modeling","time-series forecasting","causal inference","large language models (LLMs)"]},{label:"Libraries & frameworks",terms:["pandas","scikit-learn","TensorFlow","PyTorch","XGBoost","NumPy"]},{label:"Data platforms & pipelines",terms:["Snowflake","Databricks","BigQuery","Airflow","dbt","Spark","ETL/ELT","data warehousing"]},{label:"Visualization & delivery",terms:["Tableau","Power BI","Looker","dashboards","experiment design","MLOps"]},{label:"Title variants recruiters search",terms:["Data Scientist","Data Analyst","Machine Learning Engineer","ML Engineer","Analytics Engineer","Applied Scientist"]}],bullets:[{before:"Built machine learning models to help the business make decisions.",after:"Built an XGBoost churn model in Python (scikit-learn pipeline, deployed via Airflow) that flagged at-risk accounts 6 weeks early, informing retention offers that cut churn 1.8 points.",note:"Names the algorithm, the stack, the deployment path, and ties the model to a business number — the pattern data-hiring managers scan for."},{before:"Created dashboards and reports for stakeholders.",after:"Modeled marketing attribution in dbt on Snowflake and shipped a Looker dashboard used weekly by 40+ stakeholders, replacing a 2-day manual reporting cycle.",note:"Tools verbatim (dbt, Snowflake, Lo
1oker), an adoption number, and the process it replaced — impact without inventing revenue."},{before:"Ran A/B tests on the product.",after:"Designed and analyzed 15+ A/B tests (power analysis, sequential testing guardrails) on a 2M-user funnel; the winning checkout variant lifted conversion 4.3%.",note:'Shows experimental rigor ("power analysis") plus scale and a measured lift — separating you from "ran tests" resumes.'}],tips:[{title:"Match the role's altitude",body:`A "Data Analyst" JD emphasizes SQL, dashboards, and stakeholder reporting; an "ML Engineer" JD emphasizes deployment and pipelines. Mirror the posting's emphasis — the same project can be described at either altitude honestly.`},{title:'Name algorithms and libraries, not just "ML"',body:'"Machine learning" alone matches the weakest filter. Postings list concrete terms — XGBoost, PyTorch, scikit-learn, NLP — so your bullets should too, where true.'},{title:"Both the phrase and the acronym",body:`Write "natural language processing (NLP)" and "large language models (LLMs)" once in full — different companies' filters match different halves.`},{title:'Every model needs a "so what"',body:"PMs and recruiters reading past the filter want the decision the model changed. Accuracy metrics alone read as coursework; pair them with the business action they drove."}],faqs:[{question:"Which keywords do data scientist resumes need?",answer:"The posting's exact mix — typically Python/R/SQL, the ML vocabulary it uses (machine learning, NLP, forecasting, A/B testing), its libraries (scikit-learn, PyTorch, XGBoost), and its data platform (Snowflake, Databricks, BigQuery, Airflow, dbt). Paste the JD into the scanner above to see precisely which of its terms your resume is missing."},{question:"Data Scientist vs Data Analyst vs ML Engineer — does the title on my resume matter?",answer:"Yes — recruiters search titles literally. Use the standard title closest to what you did (and to the role you're targeting), and mention adjacent variants naturally in your summary if they're accurate."},{question:"Is this really free with no signup?",answer:"Yes. The scan is unlimited and runs entirely in your browser — no account, no email, no scan cap. A free account only adds extras: fixing every weak bullet with AI, tailoring to a specific job, and score history."},{question:"Is my resume uploaded or stored anywhere?",answer:"No. The scan is computed locally in your browser — your resume text is never uploaded, stored, or used for training. If you choose the optional AI sample rewrite, only that single bullet point is sent for processing."}],relatedPages:[{label:"ATS Checker (all roles)",href:"/ats-resume-checker"},{label:"For Software Engineers",href:"/ats-resume-checker/software-engineer"},{label:"For Product Managers",href:"/ats-resume-checker/product-manager"},{label:"Is It Your Resume or the Market?",href:"/tools/resume-or-market"},{label:"Jobscan Alternative",href:"/jobscan-alternative"}]}});export{y as AtsCheckerDataScientistPage,y as default};

Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.