Data & AI skills for tech, media and platforms

Tech companies, online platforms and modern publishers are data-native. The tooling is mature, the data team is strong, and nobody has to be convinced that the numbers matter. That is exactly why the usual literacy pitch lands badly here.

The real constraint sits around the data team. Product managers, growth marketers, editors and commercial leads consume dashboards and experiment results all day, and the quality of their decisions depends on whether they can read a result properly. Knowing when a test is not significant, when a funnel drop is seasonality, when an uplift is cannibalization. Getting that wrong at platform scale is expensive in a way it simply is not elsewhere.

The second thing is pace. What an AI tool can do shifts every quarter, so a fixed curriculum is out of date before the second cohort starts. And the workforce is spread across markets and time zones, remote-first, which rules out anything built around a classroom.

In a data-native company the behavior that matters is unglamorous: reading a result honestly, including the ones that say nothing happened. Knowing when a test is not significant, when a funnel drop is seasonality, when an uplift is really cannibalization. At platform scale that judgment is worth more than any tool.

It takes knowledge of what an experiment can and cannot show, skill in the tools your teams already live in (at Skyscanner that meant Tableau and Amplitude with real Skyscanner data) and, for engineering and product teams, genuine fluency with AI-assisted work rather than a demo. That runs the full range from prompting and context engineering to agentic patterns and evaluation, and it is rebuilt as the tooling moves rather than reused from a catalog.

Because the workforce is remote-first and spread across markets, adoption is designed for that: cohorts across time zones, champions in each market, and a handover so it keeps running. Growth is measured from a baseline before the start and benchmarked against other organizations.

Skyscanner put more than 1,400 employees worldwide through a tailored program built on their own data, reaching 77% completion and a visible rise in the daily use of their data products.

Just Eat Takeaway upskilled a fast-growing global workforce across ten data and AI courses, reaching 60-90% completion and 22% measured data skill growth. Skyscanner’s program was recognized at the DataIQ Awards, and Just Eat Takeaway’s was a finalist at the British Data Awards 2025.

Both our founders worked in this sector before starting Data Booster: Nanne Veldman at Ultimaker and Wouter Neef at Uber. That is where the bias towards experimentation and measurable behavior change comes from, rather than towards course catalogs.