Vice President, Translational Computational Biology

Recursion•Salt Lake, UT

13h•$319,000 - $413,600•Hybrid

About The Position

As the Vice President of Translational Computational Biology, you’ll set the strategy and drive the implementation of connecting data from our genome-scale -omics platforms with population- and patient-derived genetics and leveraging advanced analytics including ML/data science to identify novel biological and chemical hypotheses and to validate those insights as they progress rapidly and efficiently through our drug development funnel. As the VP, Translational Computational Biology, you will report to the SVP Clinical Development & Data Science, joining a diverse leadership team spanning research, development, data science, machine learning, computational chemistry, and computational biology across Recursion. You will collaborate with peer leaders in data science, biology, chemistry, and engineering to drive both present execution and future industrialization in both Recursion’s core therapeutic areas of oncology, neuroscience and future areas. The Recursion OS is our platform for the industrialization of drug discovery, linking digital and physical workflows to automate therapeutic program initiation and optimization in a scaled, data-driven manner. These workflows automatically validate biological insights from high-dimensional in vitro profiling and optimize chemical matter through loops of experimental data and ML predictions. They are powered by patient datasets and Recursion’s “maps of biology” – a unique system integrating genome-scale reverse genetics and chemical profiling in a high-dimensional, uniform biological assay to predict trillions of relationships between biological perturbations and therapeutic candidates.

Requirements

7+ years applying expertise in computational biology in service of advancing drug discovery and drug development programs. Particular consideration will be given to candidates with experience in oncology.
Experience working in partnerships with both wet-lab and computational components
Demonstrated ability to manage multiple project teams with cross-functional delivery goals
A track-record for pairing large-scale patient/population datasets in a closed loop with high-throughput in vitro functional genomics to transform the process of identifying targets, models, and biomarkers with patient connectivity and validity.
Experience in developing and implementing patient stratification / precision medicine solutions in clinical development leveraging multi-omics and clinical data and well versed in regulatory considerations.
Strong leadership of a multi-level management tree fostering an inclusive and diverse group with a passion for developing world-class talent in a matrixed environment.
Curiosity and the professional skill-set to excel in an open, highly collaborative, and growth-oriented environment.

Responsibilities

Identify and set the strategy on technological and analytical advances in the computational biology field that will directly advance Recursion's portfolio and its mission to industrialize drug development
Mentor teams to develop technologies to address specific questions that move multiple drug program and partnership projects forward
Collaborate with Recursion's research-oriented teams to evaluate new technologies and decide on which to transition toward scaled production
Lead the strategy and implementation efforts to identify optimal disease and patient linkages to our -omics phenotypes, along with the discovery and validation of translational biomarkers and patient selection solutions to drive precision medicine for our therapeutic programs
Be a key member to contribute to the strategy for the collection of patient genetic and biomarker data in Recursion clinical trials for reverse translation, while aslo being a key member to contribute to the strategy in support of the Recursion OS and clinical portfolio, building and nourishing strong links between computational biology and the larger cross-functional organization
Collaborate with groups in discovery biology, chemistry, and data science to rapidly deliver a portfolio of therapeutic programs to the clinic
Guide teams of computational biologists and data scientists in executing the above strategy to industrialize the process of turning maps into medicines