This group works as an integrated part of Research Teams to design high content experiments, to manage data, and to use advanced analytical and computational techniques to identify from back-translational studies disease endotypes and hypothesis about actionable targets therein. Furthermore, this team and the Bioinformatics Lead will collaborate with Early Development Teams to guide the development of therapeutic agents against those targets with data-driven biomarker plans through Proof of Concept (POC).
Summary of Key Responsibilities:
- Develops and executes computational strategies for disease endotyping from back-translational studies both to identify and to rank actionable targets / combinations
- Develops biomarker plans (e.g., selection, PD, efficacy biomarkers) in collaboration with project teams
- Proactively engages with project teams to define crisp scientific questions that can be addressed with appropriately powered experimental designs
- Applies scientific expertise and judgement to initiate, design, direct and execute experiments and/or studies in support of research and/or development projects. Analyzes, interprets and presents results.
- Manages and develops group of one or more computational biologists / data scientists
- Demonstrates continuous growth in depth and breadth of personal scientific knowledge and understanding of strategic goals of group, team and company.
- Works independently, pro-actively, is an impactful contributor and/or leader of project teams and transverse initiatives, and significantly advances R&D goals beyond group.
- Oversees the continued development of computational methods and pipelines needed for current and future I/O assays, including Multiplex ImmunoFluorescence (MiF), WES/WGS, RNA-seq, TCR/BCR sequencing, neoantigen prediction, CITE/REAP-Seq.
- Oversee the creation of a common informatics platform for data processing
- Ph.D. in a computational, statistical, biophysics, or bioinformatics related fields.
- A minimum of 10+years project-based work in computational application to biology related fields
- Demonstrated record of leading computational teams in biopharma
- First-hand experience at integrating publicly or commercially available genetic, genomic, and immune datasets with novel experimental data to identify testable hypotheses
- First-hand experience with and application of machine learning approaches in drug discovery and early development
- First-hand experience with and application of network analysis approaches in drug discovery and early development
- Developed communication and presentation skills and a successful track record of collaborating with cross-functional project teams
- An ideal candidate will be collaborative, self-directed and possess a positive, proactive can-do mindset
- Strong interest in cancer biology and immunological diseases
- Statistical programming skills like SAS/S-plus, R/bioconductor and pipeline pilot or equivalents
- Excellent understanding of immunology and hands-on research experience investigating complex lymphoid and/or myeloid cell subsets
- First-hand multi-parametric data mining experience for target identification and biomarker discovery. For example, multivariate analysis; dimensionality reduction methods; parametric and non-parametric statistical methods; Bayesian statistics; pattern recognition or classification methods.
- Strong interpersonal influencing and collaboration skills to work in a team-oriented, matrix environment, and the ability to work through conflicts
- Demonstrated ability to lead projects of moderate scope, mapping-out critical path, milestones and timelines, as well as manage customer expectations with minimal supervision
- Ideally have industry experience with a record of effective project leadership
- Ability to initiate and drive external collaborations and engage key opinion leaders
- Expertise in Genetics desirable