VP, Cardiometabolic Disease - Translational Genetics

The Opportunity

We seek an exceptional, visionary leader to serve as Vice President, Cardiometabolic Disease - Translational Genetics (Human Genetics and Target Translation). This role is a critical link between our foundational AI/ML‑driven target identification platform and the acceleration of our therapeutic pipeline. You will leverage your deep expertise in human genetics and drug development to convert novel, genetically‑validated targets into viable drug programs.

You will report to the SVP, Metabolic Disease and Translational Pharmacology and can be either onsite (3 days per week) in our South San Francisco office or hybrid (~1 week per month) in our office.

• Strategic Leadership: Define and execute the strategy for leveraging human genetics and ML‑imputed phenotypes (e.g., from large‑scale human cohort data combined with proprietary multi‑modal cellular data) to identify and prioritize novel therapeutic targets for cardiometabolic diseases (e.g., heart failure, kidney disease, MASLD/MASH, obesity).

• Target Validation & Conversion: Lead the cross‑functional efforts to validate and transition unique genetic targets identified by the AI/ML platform into actionable drug discovery programs. This includes establishing rigorous criteria for target validation, developing translatable disease models, and providing expert input to drive programs from Target Identification to Lead Identification
  .

• Target Imputation & GWAS Support: Partner closely with our Machine Learning and Computational Biology teams to validate and leverage AI/ML models for imaging based phenotypes of interest to empower genetic discovery. This data will be used to support high‑powered Genetic Association Studies (GWAS & RVAS) analyses for target identification.

• Cross‑Functional
  
  Collaboration:
  
  Serve as the subject matter expert and strategic partner to Research, Platform Biology, and Drug Discovery leaders. Guide experimental design (e.g., cell‑based models, high‑content screens) to generate high‑quality, fit‑for‑purpose data essential for training and validating ML models and confirming target engagement.

• Pipeline Advancement: Ensure the efficient progression of targets through preclinical stages, making key go/no‑go decisions based on genetic evidence, biological validation, and drug tractability.

• Team Leadership: Lead, mentor, and grow a high‑performing team of scientists and analysts focused on human genetics and target translation.

• MD/Ph.D (preferred) or Ph.D. in Human Genetics, Statistical Genetics, Biology, Pharmacology or a related discipline with at least 12+ years of relevant experience in target and drug discovery and development in cardiometabolic disease.

• Exceptional Track Record: Proven success in leading teams to convert unique, genetically‑driven insights into validated drug targets
  , evidenced by a significant contribution to advancing programs toward or into preclinical and clinical development.

• Cardiometabolic Expertise: Track record of familiarity with the treatment of cardiometabolic disease and relevant pathogenic mechanisms
  , preferably with a focus in heart failure, and other cardiovascular diseases.

• Human Genetics Expertise: Deep, practical experience with the application of human genetics/GWAS/RVAS to target discovery and selection
  , including understanding of Mendelian Randomization, PRS, fine‑mapping and functional annotation and interpretation, meta‑analysis, and the integration of diverse clinical and molecular datasets.

• Experience with large‑scale human genetics biobank, e.g., Finngen, UKB, All of Us

• Clinical informatics experience a plus (phecodes, ICD code etc.)

• Drug Discovery Acumen: Extensive knowledge of the entire drug discovery lifecycle, including target validation, assay development, lead identification, and the requirements for advancing small molecules, biologics, or oligonucleotide‑based therapeutics.

• AI/ML Fluency: Strong familiarity with the principles and practical deployment of AI/ML to biological data (genomics, omics, imaging) and the ability to effectively collaborate with computational scientists on model development and data generation strategies.