Klarvo.

Fractional data science lead for retail and e-commerce

E-commerce and marketing teams want data, but there's no roadmap for infrastructure, analysis and modeling, and a single analyst tries to keep up with everything.

▶ The solution

If you have no data team, or are just building one, I act as a part-time senior data lead: setting priorities, running projects and helping you build the team when it's needed.

▶ Use case

I draw up the data roadmap, lead the analyst technically, and we decide together which work stays in-house and which goes outside.

▶ Data you need

To start, the current data sources, reports and the questions teams ask most.

The industry's data landscape

In retail and e-commerce, data is spread across point-of-sale systems, the e-commerce platform, loyalty programs and marketplace dashboards. Transaction volumes are high and product ranges wide, so even small improvements in rates add up to a visible difference.

A fractional data science lead takes on the senior data leadership role in your company on set days of the week. They own the data and AI roadmap, prioritize and run projects, and guide the existing team if there is one.

The math behind it

Priority score (RICE) = Reach × Impact × Confidence / Effort

  • Reach: people or transactions affected per period
  • Impact: benefit to each (scaled score)
  • Confidence: how sure you are of the estimates (%)
  • Effort: work required (person-months)
Companies without a data team rarely lack ideas; they lack an order. A simple score like RICE [1] ties which data project starts first to a shared measure instead of personal preference.

How I work

  1. First month

    Situation and priorities.

    Data, systems, teams and ongoing projects are reviewed. The work that contributes most to business results is identified and a written roadmap comes out.

  2. Following months

    Running the projects.

    Prioritized projects get delivered, and where needed I do part of the work myself. Management gets regular, readable progress reports.

  3. When needed

    Team and hiring.

    If a team is to be built, role definitions are written, candidates are assessed technically, and new joiners are supported as they start.

  4. At the end

    Handover.

    The goal is not to stay forever. When a team or a full-time lead is ready, responsibility is handed over with the roadmap, processes and documentation.

Full service details: Fractional data science lead →

▶ Questions and answers

Fractional data science lead for retail and e-commerce — what data do you need?

To start, the current data sources, reports and the questions teams ask most.

When is this the wrong choice?

You want to finish one project. A fixed-scope pilot or project is the better frame. A monthly leadership role isn't needed. You already have a strong data lead. A second lead creates confusion. If you need support on a specific topic, working per project is better.

How does this work start?

With a short intro call. I listen to what you need and tell you plainly whether your data is enough for the job and what the first step should be. I reply to every message within 24 hours.

▶ Concepts

Linked to Wikipedia and Wikidata.

▶ Sources

  1. [1]Intercom, RICE: Simple prioritization for product managers, 2016. www.intercom.com/blog/rice-simple-prioritization-for-product-managers/
  2. [2]Wikipedia, Chief data officer, 2026-10. en.wikipedia.org/wiki/Chief_data_officer

▶ Intro call

Let's talk about your situation.

In a 30-minute call I listen to what you need and tell you plainly how I can help.

Book an intro call

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