Why this role stands out
As a Founding Data Scientist at Almedia, you will pioneer innovative measurement and growth strategies, directly impacting a rapidly growing, top-tier European company. This role is ideal for a driven individual who thrives on building foundational data infrastructure and driving impactful, data-driven decisions. Join a dynamic team shaping the future of marketing and accelerate your career in a high-impact environment.
Quick overview
Job description
This isn’t your regular job. Almedia is a place where those who want to push harder can accelerate their careers faster than anywhere else. We’re aiming to become Germany’s second bootstrapped unicorn. Almedia is already Europe’s #3 fastest-growing company in 2025 (FT1000).
We are building the future of marketing by rewarding our community of over 95 million users for engaging with our advertisers’ products. We are offering a new way to acquire users for the biggest companies in the world.
Founding Data Scientist
We're looking for a Founding Data Scientist to work directly with leadership on zero-to-one measurement and growth problems. This is not a role for maintaining existing systems — it's for someone who can take an ambiguous, high-stakes question and build the data infrastructure to answer it reliably, then own what comes next.
We value people who combine measurement rigour with builder instincts: someone equally comfortable designing a causal experiment and shipping the pipeline that operationalises its output.
What You'll Do
Own incrementality testing across marketing, product, and rewards — proving its value from hypothesis to business impact.
Build the data pipelines and measurement stack behind it, tying experiments to real shifts in user behaviour, monetisation, and multi-touch attribution.
Drive experimentation strategy across growth and product: design, run, and interpret tests with a sharp focus on causal validity — not just p-values.
Own data products end-to-end: scoping, architecture, implementation, rollout.
Partner with C-level, engineering, and business stakeholders to turn measurement into live operational decisions.
Set the frameworks and practices the team can scale on.
What You Need
Deep expertise applying causal inference and experimental design in practice, in real business contexts.
Hands-on experience building and operationalising attribution models in a real business context — not just running models, but making them useful.
Proven zero-to-one experience: ambiguous problem, built the infrastructure, shipped something production-ready.
Proficiency in Python and SQL; GCP preferred; comfortable with modern data stack tools (dbt).
Experience with programmatic advertising, ML pricing, or mobile measurement — MMPs, probabilistic attribution, or similar is a strong plus.
Familiarity with AI and LLM tooling is a plus — whether that's integrating language models into data workflows, using AI-assisted analysis to move faster, or exploring how generative and predictive AI can sharpen measurement and personalisation at scale. We're an AI-native team and expect this role to grow alongside those capabilities.
You Are
A builder with a strong ownership mindset — you don't wait for a spec.
Rigorous but pragmatic: the stats matter, but the business question matters more.
Someone who, if they have to debug something twice, automates it.
A clear communicator — you can translate model outputs into decisions that non-technical stakeholders can act on.
Why Almedia?
Own Our Growth: Competitive package with meaningful equity.
High Impact: Work on company-defining projects, not tickets in a queue.
Scale With Almedia: Join a profitable, high-growth startup early.
Great Environment: Central Berlin office, transport subsidy, breakfasts and lunches, language learning, Urban Sports Club, and more.
We believe in fostering talent, evaluating all skill levels during the hiring process, and providing a clear path for growth. Almedia is an equal opportunity employer. We embrace and celebrate diversity, and encourage individuals from all backgrounds to apply.
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