Data & Analytics
Data scientists, analysts, ML engineers, and analytics leads. In 2026 recruiters want production-ready thinkers — not notebook collectors. Every project is framed as a business question, an approach, and a measurable outcome.
7 sample CVs, entry level to senior
Fictional candidates, built to the exact standard you receive. Browse all 43 samples →
What we change for data
Business question → outcome
Every project bullet follows: what the business needed to know, what you built to answer it, and what changed as a result — in numbers. Model accuracy alone is not an outcome.
Stack stated precisely
Python not "scripting languages". scikit-learn, PyTorch, XGBoost — named explicitly. dbt, Airflow, Databricks, BigQuery — each one is a keyword ATS searches for.
Production vs experimentation
Recruiters in 2026 distinguish analysts who built notebooks from engineers who deployed models to production. We make your deployment experience explicit and prominent.
GitHub and portfolio linked
Your GitHub profile and 2–3 project case studies go in the header. Each case study: problem, data source, method, business result — not just a repo link.
Scale stated
Rows processed, prediction volume, model serving latency, dataset size. Reviewers use these numbers to calibrate the complexity of your experience.
Domain expertise surfaced
Fintech, e-commerce, healthcare, logistics — your domain is a differentiator. We make it prominent so you match industry-specific searches.
What we will ask you for
Have these ready and the first draft moves faster.
- 01Current resume or LinkedIn in any state
- 02GitHub profile URL and 2–3 projects to highlight
- 03Business outcomes from each project — what changed, in numbers
- 04Full stack — languages, frameworks, cloud, orchestration tools
- 05Roles you are targeting — job descriptions or company names
Not in data?
Every other profession gets the same treatment under its own rules.