Director, Computational Biology

The Arc Institute is a new scientific institution that conducts curiosity-driven basic science and technology development to understand and treat complex human diseases. Headquartered in Palo Alto, California, Arc is an independent research organization founded on the belief that many important research programs will be enabled by new institutional models. Arc operates in partnership with Stanford University, UCSF, and UC Berkeley.

While the prevailing university research model has yielded many tremendous successes, we believe in the importance of institutional experimentation as a way to make progress. These include:

• Funding:
  Arc will fully fund Core Investigator’s (PI’s) research groups, liberating scientists from the typical constraints of project-based external grants.
• Technology:
  Biomedical research has become increasingly dependent on complex tooling. Arc Technology Centers develop, optimize and deploy rapidly advancing experimental and computational technologies in collaboration with Core Investigators.
• Support:
  Arc aims to provide first-class support—operationally, financially and scientifically—that will enable scientists to pursue long-term high risk, high reward research that can meaningfully advance progress in disease cures, including neurodegeneration, cancer, and immune dysfunction.
• Culture:
  We believe that culture matters enormously in science and that excellence is difficult to sustain. We aim to create a culture that is focused on scientific curiosity, a deep commitment to truth, broad ambition, and selfless collaboration.

Arc employs more than 300 people, with $650M+ in committed funding and a state of the art new lab facility in Palo Alto.

• Bridge the Gap Between Data and Discovery: Move beyond standard pipelines. You will lead our "Tertiary Analysis" work, transforming raw counts into mechanistic analyses that explain how genes, drugs, and environments collide.
• Join a New Type of Research Org: Work within a structure that fuses the high-velocity execution of a startup with the intellectual rigor of a world-class academic institute.
• Command "Gold Standard" Datasets: Gain exclusive access to massive, full-production datasets – from T-cells and NGN2 neurons to deep chemo genetic screens – designed specifically for large-scale discovery.
• Lead with Autonomy: While a sister team handles the essential bioinformatics infrastructure and QC, your mandate builds on top of this by developing the novel methods and analyses needed to uncover new biology (e.g. gene-module detection tools, etc).
• Open science and open source
  - we publish our breakthroughs openly to accelerate scientific progress.

• Biological Mechanism Minded: You aren't satisfied with a list of differentially expressed genes. You are obsessed with the "why": building explanatory models that map the complex interactions between genetics, compounds, and the cellular environment.
• Empathetic Leader: You enjoy and derive energy from the "human" side of science. You are a dedicated mentor who views the professional and scientific growth of your team as one of your primary metrics of success.
• Scientific Translator: You are a rare hybrid who speaks the language of both the wet lab and bioinformatics. You can debate mechanistic biology with a neuroscientist and then pivot to discuss algorithmic efficiency with a tool-builder.
• Production Scaled: You have experience working with massive, "full-production" datasets. You know how to extract biological signal from T-cell assays or chemo genetic screens at scale in a fast paced, collaborative environment.

• Build and manage Arc’s Computational Biology team.
• Actively mentor and provide guidance to research staff, developing them both professionally and scientifically.
• Develop and refine cutting-edge tertiary analysis and explanatory modeling approaches across Arc’s key research areas, including functional genomics, transcriptomics, epigenomics, and proteomics.
• Integrate multiple data types to elucidate functional connectivity between genes, transcripts, and proteins, with the goal of building mechanistic models of gene…