EMEA Head of Data Science

vor 1 Woche

Basel, Basel-Stadt, Schweiz QP Group Vollzeit CHF 120'000 - CHF 250'000 pro Jahr

Role: Head of Data Science, EMEA (Basel-Based)

Location:
Basel, Switzerland (Hybrid/On-site Leadership Required)

Team Size:
Leading 10+ Senior Data Scientists, ML Engineers & Applied Researchers

I
ndustry:
Life Sciences / R&D / Data & AI Platform

The Mission

To build, scale and lead a world-class Data Science organisation across EMEA — driving how pharmaceutical and biotech organisations generate insight from high-value scientific data. This role sits at the intersection of data, AI/ML innovation, and modern R&D workflows, with the mandate to shape scientific intelligence across the region.

What You Will Own

  • Leadership of a 10+ person EMEA Data Science organisation
    — managing senior scientists, technical leads and applied ML specialists
  • Development of
    data, AI/ML strategies and scientific analytics frameworks
    used by global pharma R&D teams
  • Oversight of
    advanced modelling programs
    including ML pipelines, foundation model development, multimodal analysis, and generative AI applications for scientific data
  • Grow the team — hiring, mentoring and developing high-calibre talent across computational biology, ML and data engineering
  • Translate scientific complexity into commercial and business impact — partnering with product, platform, engineering, commercial and customer-facing functions
  • Drive Data Science excellence across customer deployments — enabling enterprise-scale analytics, FAIR data design, workflow integration and ML-readiness
  • Represent EMEA as the scientific and technical authority in executive strategy meetings

Ideal Experience

  • 10–15+ years in
    pharma, biotech or life-science-focused data environments
  • Proven experience
    leading Data Science organisations
    (10+ heads) in drug development, R&D, translational research or scientific informatics
  • Direct leadership over machine learning, statistics, computational science and model deployment in scientific contexts
  • Experience with
    large, complex scientific datasets
    — omics, imaging, phenotypic screening, lab data, ELN/LIMS outputs, clinical or pre-clinical research environments
  • Exposure to cloud-scale data pipelines and ML productionisation (AWS, Databricks, Snowflake, Python, PyTorch/TF etc. — not essential to list but helpful)
  • Strong stakeholder influence — able to communicate science deeply while driving executive-level decisions at pace

Interested in learning more?

Apply directly or reach out to to discuss further.

Keywords;
Data Science
,
AI/ML
,
Machine Learning
,
Deep Learning
,
Drug Discovery
,
Drug Design
,
Computational Biology
,
Bioinformatics
,
Translational Science
,
Target Identification
,
Preclinical Development
,
Clinical Development
,
Biomarker Discovery
,
R&D Informatics
,
Data Engineering
,
Cloud Architecture
,
Data Pipelines
,
LLMs
,
Foundation Models
,
Generative AI
,
Predictive Modelling
,
Multimodal Data
,
Omics Analytics
,
Genomics
,
Proteomics
,
Transcriptomics
,
Single-Cell Analysis
,
High-Throughput Screening
,
Imaging Analytics
,
FAIR Data
,
Data Governance
,
Real-World Evidence
,
Computational Chemistry
,
Cheminformatics
,
Protein Modelling
,
Structural Biology
,
Molecular Dynamics
,
Statistical Modelling
,
Bayesian Inference
,
Knowledge Graphs
,
Graph Neural Networks
,
Neural Networks
,
Data Strategy
,
ML Ops
,
Model Deployment
,
Inference Pipelines
,
Scientific Data Systems
,
ELN/LIMS
,
Data Integration
,
Pharma R&D
,
Biotech
,
Life Sciences
,
AI-Driven Discovery
,
Physics-Informed Models
,
Precision Medicine
,
Therapeutic Pipeline Acceleration
,
High-Dimensional Data
,
Enterprise Analytics
,
Cloud-Native ML
,
BioPharma AI
,
Predictive Toxicology
,
Target Validation
,
AI-Optimised R&D
,
Computational Drug Design
,
Platform-Scale Analytics

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