What are the responsibilities and job description for the Computational Biologist position at Hlx Life Sciences?
Computational Biologist Opportunity - Biobanks | Berlin or New York
We’re partnering with a cutting-edge AI-driven biotech to hire a Computational Biologist who will take full ownership of evidence generation from large-scale population biobanks. This role is central to building the foundation of how biobank data powers discovery and translational decisions across the organisation.
The Role
As a Computational Biologist, you will be responsible for the end-to-end ingestion, integration and analysis of individual-level datasets within trusted research environments (TREs). You will sit within the Biobank/Evidence pillar (reporting into scientific leadership), collaborating closely with discovery and platform teams to deliver high-impact biological insights.
This is not a downstream statistical genetics or purely translational analytics role - it requires someone who has truly operated biobank pipelines at scale.
What You’ll Do
- Lead ingestion, harmonisation and integration of individual-level biobank datasets (UK Biobank, FinnGen, MVP, Estonian Biobank, etc.).
- Build and maintain scalable pipelines within TREs to enable robust evidence generation.
- Perform analyses across disease phenotypes, molecular traits and population-level signals.
- Ensure data quality, reproducibility and documentation across biobank workflows.
- Partner with cross-functional teams to translate insights into research and platform strategies.
- Drive continuous improvement of biobank-side processes and infrastructure.
What We’re Looking For
- Hands-on experience integrating population-scale biobank datasets within TREs.
- Demonstrated ownership of end-to-end ingestion and integration workflows - not limited to downstream StatsGen modelling.
- Strong computational biology background, comfortable as a generalist (data engineering → QC → analysis → biological interpretation).
- Experience from large pharma, major biobank initiatives, or academic environments where you have built and run biobank-side pipelines with minimal support.
- Ability to thrive in a fast-paced, high-autonomy research environment.
Not suitable: Candidates who have only used biobank data downstream without managing ingestion, integration or secure-environment workflows.