Head, Computational Biology & Bioinformatics

Ipsen Pharma

Cambridge, United Kingdom

This role will be tasked with the design, implementation and automation of analytic pipelines for large-scale biologic data analyses and data mining, biomarker discovery and patient stratification, etc. using large-scale omics, clinical and other large multi-modal datasets. This role requires close collaboration with preclinical and clinical teams to enable data-driven decisions across the research and development pipeline.

This is a hands-on, data-centric role ideal for someone who thrives in a fast-paced, interdisciplinary environment and wants to help build transformative capabilities in support of a fast-growing pre-clinical and clinical portfolio.

Main Responsibilities & Technical Competencies

Define and execute Ipsen’s bioinformatics and Computational Biology strategy aligned with company’s research ambitions and therapeutic and scientific priorities.
Partner with IT and R&D on implementation of an internal Bioinformatics and data and analytics environment and lead the implementation of analytics bioinformatics capabilities for large-scale analytics on a broad set of biologic data.
Collaborate with pre-clinical and clinical teams to conceptualize new analyzes and analytic approaches that will enable various modeling and simulations approaches (e.g. disease progression modelling, immune response simulation, PK/PD modelling, etc.).
Partner with translational scientists, clinicians, etc. to translate omics, preclinical, and clinical data into actionable insights and hypotheses.
Guide the integration of multi-omics data (genomics, transcriptomics, proteomics) to support target discovery and validation.
Conceptualize and develop AI/ML models for target discovery/prioritization, drug re-purposing, and predictive analytics.
Forge robust collaborations across pre-clinical, clinical, regulatory, and Medical teams to align informatics strategies with business goals.
Act as the primary thought leader and subject matter expert for bioinformatics, computational biology, and data science more broadly.
Represent the company in external scientific forums and consortia to remain at the forefront of informatics and translational science.
Stay abreast of scientific literature and integrate new insights into research activities and evaluation of new technologies and methods.
Administration:
- Lead and grow a multi-disciplinary team of experienced Bioinformaticians and Computational Biology scientists to ensure their professional growth.
- Develop team’s yearly budget and ensure appropriate financial management of team’s activities.
- Manage relationships with internal (e.g. translational research, PK/PD) and external (e.g. CRO, academia) stakeholders.
Continuous Learning and Innovation:
- Stay abreast of advancements in data science, machine learning, and healthcare technologies.
- Mentor and foster collaboration and knowledge sharing across teams.
- Educate and raise awareness around Data Science and its potential in supporting Ipsen’s internal teams to generate buy-in across the organization.
- Actively participate in relevant forums and seek to establish Ipsen as an innovator and thought leader.

HOW - Knowledge & Experience

Knowledge & Experience (essential):

10+ years of relevant experience in pharma with hands-on experience building pipelines, analyzing large-scale biologic data, etc. and leadership experience leading bioinformatics/computational biology teams.
Proven experience leading teams and scaling bioinformatics and/or computational biology function in a matrixed and fast-paced environment.
Expert skills in data science, computational genomics, and proficiency with common bioinformatics tools (GATK, Nextflow, Seaborn, Biopython, etc.) and data sources (TCGA, UK Biobank, etc.).
Extensive experience with sequencing platforms (e.g., Illumina, PacBio, Oxford Nanopore) and the design, implementation, and optimization of bioinformatics workflows for large-scale genomic data analysis.
Expertise in evaluating and selecting sequencing technologies for specific R&D or clinical trial applications
Solid understanding of human genetics, molecular biology, as well as biological pathways and networks
Strong shell scripting (bash) and R and/or Python programming skills with focus on biologic data.
Strong methodological and analytical skills with regard to machine learning, deep learning, NLP and other AI approaches. Proven experience applying these approaches in healthcare space.
Strong communication skills and the ability to convey complex technical concepts to diverse audiences.