Consulting

Focused projects with an answer at the end

I take on a small number of projects at a time, each scoped around one decision or one system. You work with me directly, from framing the question to handing over something your team owns.

“Is our marketing spend actually driving growth?”

Measurement and experimentation

For growth, marketing and finance teams

Sound familiar?

  • Every channel report claims the same sales.
  • A/B tests run, but nobody fully trusts the readouts.
  • Budget decisions still rest on last-click attribution.

What I do

Design and run incrementality tests and experimentation programmes, build attribution you can reason about, and help your team read results honestly, so budget moves to what causes sales rather than what correlates with them.

“It works in the demo. Will it work for every user?”

Search, LLM and agent systems

For product and engineering teams

Sound familiar?

  • The assistant impressed in the demo and drifts in production.
  • Every prompt or model change feels like a gamble.
  • When a user reports a bad answer, nobody can reconstruct why.

What I do

Evaluation harnesses that gate releases, retrieval and ranking improvements measured offline and online, and tracing that ties every answer to the prompt, model and context that produced it.

“We have years of process data and a lot of tacit know-how.”

ML for science and industry

For R&D and engineering leads

Sound familiar?

  • Process parameters live in a few experts' heads.
  • Test and production data pile up, and little of it is used.
  • Generic ML tools ignore the physics of your process.

What I do

Models and optimisation grounded in the physics of the process, for laser, photonics and other physical systems, from someone who has aligned the lasers herself.

How engagements work

Small, fixed scope first. Then build.

Every engagement starts with a free 30-minute call to check whether I am the right person for your problem.

  1. Diagnostic

    Two weeks, fixed fee. I look at your data, your setup and the decision you need to make, and hand back written findings with a prioritised plan.

  2. Build

    Six to eight weeks on one outcome: a measurement programme running, an evaluation harness in production, a model your team owns.

  3. Continue

    Optional monthly support with capped days and a written response window, for teams who want a senior partner on call.

What you can expect

How I work

Evidence before opinions

Every recommendation comes with how we would find out it is wrong.

Uncertainty stated plainly

Assumptions, error bars and limits are written down, not buried.

Deliverables your team owns

Code, documentation and a proper handover, in your stack.

Direct and collaborative

You work with me, not a junior team, in English, French or German.

Selected work

Problems I have solved before

eBay · Growth marketing

Which marketing spend actually brings new buyers

Designed and analysed A/B tests and incrementality studies for paid search and coupon campaigns in the US, UK and Germany, separating the buyers each campaign really brought in from those who would have come anyway.

eBay · Retention

Knowing which customers are about to leave

A churn model scoring every one of eBay's 130 million customers each day, accurate enough (F1 0.90) for retention teams to target their emails at the people most likely to leave. Owned from the first question to handover.

eBay · AI assistant

An AI shopping assistant customers can rely on

Evaluation cycles that raised the assistant's answerability to 89% and its resolution rate to 92%, a safety gate that stops 98% of unsafe requests, and personalised prompts with 8× the click-through of generic ones.

What it taught me →

eBay · Personalisation

Homepage recommendations that earn more clicks

Rebuilt the Recently Viewed Items module on eBay's homepage to hide items already bought and near-duplicates, lifting click-through from 5% to 6% among the most active customers.

eBay · Customer insight

One shared picture of every customer

A segmentation of the full customer base by expertise and engagement, updated daily. Marketing teams in four markets adopted it for targeting, and production recommendation systems use it as an input.

ETH Zürich · Research

Reliable signals from very noisy measurements

PhD extracting attosecond timing signals from millions of noisy measurements, with the uncertainty of every result quantified. Published in Science and Nature Physics; the same discipline I bring to industrial and scientific data.

Questions

Before we talk

Do you sign an NDA?

Yes. I sign a standard NDA before looking at any data, and your data stays in your systems unless we agree otherwise.

Do you work remotely or on site?

Mostly remotely, with on-site sessions in the Zürich area when working side by side helps, for example to kick off a project or hand it over.

What do you need from us?

One person who owns the question, read access to the relevant data, and two short check-ins a week while the project runs.

Which tools do you work with?

Yours. I work in Python and SQL on the warehouse and cloud you already use, so everything keeps running after the engagement ends.

What size of company do you work with?

I work best with teams where data decisions carry real money but there is no large data science team yet, and with larger teams that need a senior specialist for one well-defined problem.

What happens after the first call?

If there is a fit, I send a one-page proposal for the diagnostic with scope, dates and a fixed fee. Nothing starts until you have agreed to it in writing.

Have a question you need answered properly?