I'm a data scientist and growth strategist working at the intersection of statistics, engineering, and marketing. My clients are companies that have moved past "let's try it and see" — they want the numbers, the methodology, and the confidence that comes from doing the work properly.
Background
M.Sc. in Industrial Engineering & Management from Tel Aviv University. Ten years working across paid media, analytics, predictive modeling, and marketing automation. I've optimized more than 120 campaigns and worked with over 50 million data points across e-commerce, SaaS, finance, and B2B.
How I work
Every engagement starts with an audit — of the data, the tracking, the assumptions, the objectives. Only then do we agree on what "success" actually means and how we'll measure it. That framework guides every decision that follows.
I'm tool-agnostic in philosophy and deeply expert in practice: SQL, Python, GA4, GTM, BigQuery, Meta Ads Manager, Google Ads, Looker Studio, and the modern marketing automation stack. But tools are secondary. What matters is the question you're actually trying to answer.
What clients get
- Transparency. You see the methodology, the assumptions, the confidence intervals. No black boxes.
- Rigor. Every recommendation is backed by statistical significance, not vibes.
- Speed. Weekly cadence, not quarterly deliverables. Decisions happen when they need to happen.
- Focus on unit economics. Vanity metrics don't pay salaries. Every model I build serves a commercial question.
Who I work with
Companies that already have some data, some tracking, and some intuition — and are ready to replace the intuition with evidence. Typical engagements range from single-project audits to ongoing fractional data-science partnerships.