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

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.

What the job looks like

Every day you will enjoy different challenges such as:

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 establishing 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 and analysis.
• Oversight of the data attributes and metadata architecture across the research database suite, for example ELN such as Revvity Signals and data factory such as 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 and 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.

Leadership & Collaboration

• Collaborate with internal experts across research functions and external CROs and vendors to onboard data ingestion solutions.
• Align 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 and stakeholders.

What you'll bring to the table

• PhD in quantitative field, for example computational biology, mathematics, statistics or physics, with significant biological background, or a PhD in life sciences such as 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 and 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 and ability to present complex computational methods to non-experts.
• Outstanding organizational skills and ability to work independently.

Technical Skills

• Programming and scripting: Python and/or R, Shell, Linux/Unix.
• Data science libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Plotly, R and Bioconductor packages.
• Data management and governance: ELN such as Revvity Signals or Benchling, SQL, Spark, Databricks, Snowflake, BigQuery, Azure Synapse, Airflow and Prefect.
• Visualization and dashboarding: Spotfire, Tableau, Power BI and custom Python dashboards such as Plotly.
• Workflow orchestration and management: Airflow, Prefect, Databricks Jobs, Conda, Poetry, Docker, Singularity, Nextflow and Snakemake.
• Cloud platform architecture: Azure, Microsoft suite and Databricks.
• Data architecture and modeling: relational and dimensional modeling, schema design for experimental data, assay registries and compound or biological entities.
• Ontology and semantic layer: controlled vocabularies and ontologies, OBO, ChEBI and Gene Ontology.
• ETL/ELT and integration: advanced SQL, Python, pandas, pySpark and Spark.
• MLOps and operational excellence: MLflow or Weights & Biases for experiment tracking; CI/CD with GitHub Actions or Azure DevOps for automated pipeline testing.

Desirable experience

• 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 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.

About Grünenthal

Grünenthal is a global leader in pain management and related diseases. As a science-based, privately-owned pharmaceutical company, we have a long track record of bringing innovative treatments and state-of-the-art technologies to patients worldwide. Our purpose is to change lives for the better – and innovation is our passion. We are focussing all of our activities and efforts on working towards our vision of a world free of pain.

Grünenthal is headquartered in Aachen, Germany, and has affiliates in 28 countries across Europe, Latin America and the US. Our products are available in more than 100 countries. In 2024, Grünenthal employed around 4,300 people and achieved sales of € 1.8 billion.

Website: www.grunenthal.com