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Case study / 01

Global Lead, Talent Acquisition
2022 – 2025

ZALANDO

An AI organization from zero to 120 people in six months

Built a pan-European AI organization from zero to 120 people across four countries, led a 22-person talent team across Europe and China, and improved hiring speed and offer acceptance.

AI organization in six months
0 → 120
Time to Hire
−32%
Offer acceptance
+21%
Interviewers trained
1,000+

01 · The mandate

Europe's leading fashion platform was making its big bet on AI. It needed an entire cross-functional AI organization — research, ML engineering, product — built from nothing, at speed, across markets it hadn't hired in before.

02 · What I built and led

I led talent acquisition globally across AI and machine learning, Research, Technology, Commercial, Product, Design and corporate functions — a team of 22 across Europe and China.

The AI build-out went from zero to 120 people in six months across Germany, Ireland, Switzerland and Finland. Under the organization’s internal reporting definition, 42% were diversity hires. The work included market entry, executive search for the leadership team, and standing up a Technology and AI hub in Shenzhen.

Beyond the build-out: launched AI/ML and Research early-careers programs and an Associate PM MBA program, and built an interviewer training system grounded in hiring data and neuroscience — over 1,000 interviewers trained.

The system-level results: Time to Hire down 32%, final-stage-to-hire conversion up 16%, offer acceptance up 21%. Rated in Zalando's top performance tier (~3% of the org).

How it worked

Reconstructed operating model

A talent system built around the organization—not a list of vacancies.

The build linked capability planning, market entry, leadership search, talent pipelines and interviewer quality into one operating loop across four countries.

  1. 01

    Capability map

    Translate the AI strategy into leadership, research, engineering and product capabilities.

    Team
  2. 02

    Market entry

    Design a hiring approach for Germany, Ireland, Switzerland and Finland.

    Human judgment
  3. 03

    Talent engine

    Build repeatable pipelines and executive search around the capability plan.

    Operating system
  4. 04

    Quality loop

    Train 1,000+ interviewers and use conversion data to improve the process.

    Operating system
  5. 05

    AI organization

    Build the leadership team and 120-person cross-functional organization in six months.

    Team
Durable outcomeA repeatable cross-market talent system remained: leadership, pipelines, trained interviewers and improved outcomes at every major stage.

03 · Tradeoffs and judgment

The choices that shaped the system.

  1. 01

    Build the leadership team first

    Executive search and capability planning set the shape of the organization before volume hiring accelerated.

  2. 02

    Treat each market as a product

    The four-country build needed local entry strategies connected to one global operating model—not a copied sourcing playbook.

  3. 03

    Fix conversion, not just volume

    Interviewer training and funnel measurement made quality and speed part of the same system.

04 · What changed

Scale and speed at the executive level: building an entire AI organization, not filling roles.

Evidence note · Metrics are drawn from the operating record for this work. The diagram is a confidentiality-safe reconstruction, not an internal Zalando artifact; selected references and supporting context are available privately.