About this template
The ATS Data Analyst template combines a modern layout in Inter for body text and Source Code Pro for figures, on a clean blue-tinted background. Skills sit in plain text but key metrics — KPI lifts, dataset sizes, model accuracy — get a code-style emphasis recruiters scan in seconds. Compatible with ATS engines at SaaS vendors (Lever, Ashby, Greenhouse) and large enterprises (Workday at Walmart, Avature at JPMorgan Tech).
Who is it for?
It fits data analysts, BI specialists, ML engineers and data scientists applying to product companies (Stripe, Datadog, Snowflake, Databricks, Anthropic), banks (Goldman Marquee, JPMorgan AI Research), consultancies (McKinsey QuantumBlack, BCG GAMMA, Bain Vector) and data-driven scale-ups. Also fitted to analytics translators and growth analysts whose value is measured by impact numbers rather than prose.
How to use it
Build a dense 'Technical stack' section with categories: Languages (Python, R, SQL, Scala), Cloud (AWS, GCP, Azure, Snowflake, Databricks), Data tools (dbt, Airflow, Spark, Kafka, Fivetran), ML (scikit-learn, PyTorch, TensorFlow, MLflow, Hugging Face), Visualisation (Tableau, Looker, Power BI, Metabase, Hex). For each role, frame bullets around business impact: 'Improved churn model AUC from 0.71 to 0.84 — 12% churn reduction — $4.8M revenue preserved'.
Frequently asked questions
Should I list Kaggle or data competitions?
Yes for 0-5 year profiles, it helps clear the screen. Mention the top percentile (top 5%, top 1%, Kaggle Expert/Master/Grandmaster) and the competition focus. For senior profiles (8+ years), keep only the major distinctions and shift the focus to internal projects with measured business impact — that is what hiring managers at FAANG and top SaaS care about.
How do I quantify the impact of a dashboard?
Find the business metric behind the tool: 'Built a real-time SLA dashboard — adopted by 240 ops managers — 38% MTTR reduction over 6 months'. ATS engines scan numbers inside bullets and recruiters often filter by order of magnitude of impact. A metric without a denominator is ignored — always provide context (population size, baseline, time window).
Should I include a GitHub or Kaggle link in the header?
Yes, this is expected in data/ML. Add 2-3 links max: GitHub (with 3-4 pinned repos on completed projects), Kaggle profile, LinkedIn. Avoid links to abandoned competitions or forks without contributions. The GitHub profile is read by Lead Data and engineering managers before the technical interview, so curate it for what you want highlighted.
