As an Associate Scientific Director, Translational Science working on the Translational Science team, you will be empowered to establish a collaborative and influential role within Immuno-Oncology Drug Discovery project teams and provides scientific contributions which have significant impact on portfolio progress , and a typical day will include:

OBJECTIVES:

Provides scientific insight from large and diverse biological datasets. Works with a dedicated team of scientists to form data driven decisions, propose actionable solutions to be tested in the lab or clinic, and to identify and progress novel cancer therapeutics. Contributes to our mission to provide innovative immuno-oncology therapies to patients across our pipeline of novel therapeutics including small molecule, large molecule and cell-based therapies. Drives change across the entire drug discovery spectrum from target identification and validation through early clinical proof of concept, formulating translational hypotheses that inform pharmacodynamic and predictive biomarkers.

Scientific leader with deep subject-matter expertise, driving project team progression and development of technical capabilities and supporting development of junior team members.

ACCOUNTABILITIES:

• Applies computational methods to integrate, visualize and interpret data resulting from whole genome, epigenome, transcriptome, proteome, single cell and efficacy studies from preclinical mouse in-vitro, ex-vivo or in-vivo studies and preclinical human in-vitro and ex-vivo studies.
• Establishes a collaborative and influential role within Immuno-Oncology Drug Discovery project teams comprised of cell biologists, biochemists, pharmacologists and translational scientists. Is responsible for analysis to deduce, summarize and communicate results directly contributing to the understanding of drug indication, mechanism of action, identification of response predictors and translational biomarkers. Works collaboratively with biologists and clinicians to realize clinical benefit from their work.
• Works collaboratively with data scientists and the information technology group to build knowledgebase solutions within our AWS computational infrastructure capable of performing and communicating genome scale data analysis on abundant proprietary and comprehensive public data, especially focused on forward and reverse translation challenges.
• Plays a leading scientific role on project team(s), setting high standards for rigor of thought
• Provides scientific contributions which have significant impact on portfolio progress or development of technical capabilities, based on benchmarking to industry best-in-class
• Acts as internal key opinion leader for his/her area of technical expertise and serving in a development capacity for junior team members
• Supports or identifies BD opportunities to enhance project progression, or functional capability expansion, with clear application to specific portfolio needs
• Continual development of scientific expertise, particularly through external interactions, linked to specific deliverables
• Contributes to development and implementation of scientific strategy, through tangible deliverables; challenges dogmas and internal ‘blind spots’; actively pressure-tests ideas cross-functionally and via external network
• Establishes a strong external reputation, through scientific publication and presentation
  Note: Job responsibilities are progressive and cumulative

EDUCATION, EXPERIENCE, KNOWLEDGE AND SKILLS:

• PhD degree in a scientific discipline with 10+ years of experience in computational biology, or MS with 14+ years of experience in computational biology, or BS with 16+ years of experience in computational biology
• Outstanding expertise and depth of knowledge within a scientific area
• Keeps up with the up-to-date scientific advancement (e.g. competitive landscape, new technology, new research portfolio, and new partnership)
• Demonstrates well-developed knowledge of other disciplines, and departments and how they function together. In particular, the candidate should have extensive experience working closely with biologists or clinicians to influence experimental design.
• The ideal candidate would have:
  • Experience with genome scale or big data analysis derived from Next Generation Sequencing including; Exome, RNA, miRNA, Copy Number, Epigenetics, Single Cell and synthetic lethal screens, as well as a track record of using preclinically derived translational hypotheses to influence clinical trial practice.
  • Data mining, text mining, systems biology and visualization solutions including: RStudio, Shiny, Omicsoft Array Studio, Spotfire, MetaCore, Ingenuity and MSigDB.
  • Programming, AWS cloud computing and command line expertise; R, SQL, Perl, Python.