Data Science Platform & Analytics Lead (m/f/d)

Data Science Platform & Analytics Lead (m/f/d)

City: Various Locations

Department: Research & Development

Job ID: 45092

Professional | Permanent | Full-time | Hybrid

If you’re passionate about changing lives for the better, this is the opportunity you’ve been waiting for. In Research & Development , we’re continuously exploring innovative new treatment options to make a stronger, more positive impact on the lives of the patients we serve. You’ll work with talented colleagues in a state‑of‑the‑art Research & Development environment, developing innovative medicines that change the life of patients for the better and help us make progress towards our vision of a world free of pain. Join us today, and discover the difference you can make.

What the job looks like

• Data Management & Governance
  • Manage end-to-end data lifecycle within Research from ingestion to integration, including, defining data standards and developing data upload templates and establish frameworks for optimal governance, data quality, metadata, and lineage.
  • Business ownership of Research data management platforms and data stewardship, including management of day-to-day operations for data handling, analytics, and platforms.
  • Build, manage, and continuously improve research databases to enable centralized, structured, and accessible preclinical data storage and retrieval.
• Platform Strategy & Architecture
  • Define and execute the research strategy and roadmap for data, analytics and AI platforms, ensuring scalability, and compliance across diverse functions, including, biology, pharmacology screening and translational assays, chemistry, DMPK, toxicology and genetic medicine.
  • Focus on FAIR data principles in the processing of internal and external data sets, data organisation and metadata capture to enable efficient downstream data dissemination, exploration, integration & analysis.
  • Oversight of the data attributes and metadata architecture across the research database suite (e.g. ELN e.g. Revvity Signals, data factory e.g. Azure Datafactory) and act as a key point of contact for change requests.
• Analytics Enablement & Platform Adoption
  • Provide direct, hands‑on support to scientific teams in organizing, structuring, and preparing experimental data for analysis, database upload and reporting.
  • Demonstrate experience in data visualization and display, integrating diverse data sets into visually accessible and understandable forms for scientific and business stakeholders via state‑of‑the‑art framework.
  • Drive global rollout and adoption of Research platforms, including ELN and project dashboards; define KPIs and success metrics to measure performance, ROI, and operational impact.
• Analytics to Drive Portfolio Decision Making
  • Generate insights and models from multi‑modal datasets (preclinical – in vivo, in vitro & clinical) to elucidate patterns, trends and relationships within data to inform R&D portfolio decision‑making.
  • Partner with domain experts to establish automated, robust and efficient analytical pipelines for reproducible research and to champion the integration of data science into biological discovery.
  • Develop and implement state‑of‑the‑art statistical, ML and AI methods for large scale data processing and analysis.
• Collaborate with internal experts across research functions and external CROs + vendors to onboard data ingestion solutions.
• Aligning with R&D and IT stakeholders on strategic data priorities, acting as a trusted advisor and data specialist.
• Communicate business impact, change and outcomes effectively to executive leadership & stakeholders.

What you'll bring to the table

• PhD in quantitative field (e.g. computational biology, mathematics, statistics, physics) with significant biological background, OR a PhD in life sciences (genetics, RNA biology, oligonucleotides, gene therapy, or other genetic medicines) with significant computational experience.
• Minimum 5 years of pharma, techbio or biotech experience creating data science & analytic solutions to enable preclinical research particularly in relation to in vitro and in vivo biological assays for SME and/or Genetic Medicine drug development programs.
• Demonstrated leadership in defining end-to-end data science and computational strategies, integrating diverse high‑dimensional datasets, and implementing advanced analytical solutions.
• Proven ability to guide teams and external partners in building reproducible pipelines, scalable data architectures, and robust infrastructure for high‑performance analytics.
• Extensive knowledge of biomedical data management and curation, including exposure to laboratory information management system (LIMS) and electronic lab notebooks (ELNs).
• Strong collaboration skills and ability to work as part of a team in an international and interdisciplinary environment.
• Excellent communication skills. Ability to present complex computational methods to non‑experts.
• Outstanding organizational skills and the ability to work independently.
• Technical Skills
  • Programming & scripting: Proficiency in Python and/or R, Shell (Linux/Unix).
  • Data science libraries: Python: Pandas, NumPy, Scikit‑learn, Matplotlib, Plotly; R & Bioconductor packages.
  • Data management & governance: ELN e.g. Revvity Signals or Benchling, SQL, Spark, Databricks, Snowflake/BigQuery/Azure Synapse, Airflow/Prefect.
  • Visualization & dashboarding: Spotfire, Tableau, Power BI, custom Python dashboards (e.g. Plotly); UX for dense bio data.
  • Workflow orchestration & management: Airflow/Prefect/Databricks Jobs, Conda / Poetry, Docker / Singularity, Nextflow, Snakemake.
  • Cloud platform architecture: Proficiency navigating and utilizing Azure/Microsoft suite and Databricks.
  • Data architecture & modeling: Relational + dimensional modeling; schema design for experimental data, assay registries, and compound/biological entities.
  • Ontology & semantic layer: Controlled vocabularies, ontologies (OBO, ChEBI, Gene Ontology).
  • ETL/ELT & integration: SQL (advanced), Python (pandas/pySpark), Spark.
  • MLOps & operational excellence: MLflow/Weights & Biases for experiment tracking; CI/CD (GitHub Actions, Azure DevOps) for automated pipeline testing.
• Expertise in RNA biology and oligonucleotide design (ASOs, siRNAs, or related modalities), with a strong grasp of sequence optimization, activity prediction, and off‑target analysis.
• Experience applying next‑generation sequencing methods—such as RNA‑seq, long‑read sequencing, RNA structural mapping (e.g., SHAPE), or lncRNA profiling—alongside bioanalytical techniques generating gene and protein expression data at bulk, single‑cell, and spatial resolution to inform discovery and translational programs.