Associate Principal Scientist, Cardio-Renal Disease Biology
Listed on 2026-03-01
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Science
Research Scientist, Data Scientist
Job Description
The Cardiometabolic Disease (CMD) Discovery Biology department at our Research & Development Division is seeking a highly innovative and rigorous Associate Principal Scientist with deep expertise in cardiovascular and renal (cardio‑renal) disease biology. The successful candidate will provide scientific leadership for one or more discovery programs, driving work from mechanistic pathway discovery and target identification through target validation and translational strategy.
This role sits at the interface of in vivo discovery biology (primary emphasis) and human genetics/omics‑enabled target discovery and translational biomarker strategy, partnering closely with cross‑functional teams to generate decision‑grade data packages for program advancement.
- Lead the design, execution, and/or oversight of in vivo studies in relevant renal and cardiovascular disease models (e.g., CKD/DKD, inflammation/fibrosis, heart failure, vascular dysfunction, ASCVD‑relevant biology), including rigorous experimental design and quantitative phenotyping.
- Develop and implement end‑to‑end in vivo pharmacology/biology strategies to evaluate target hypotheses and therapeutic concepts; ensure high‑quality data generation, analysis, and reporting.
- Establish and/or oversee complementary in vitro and ex vivo assays (e.g., primary cell systems, iPSC‑derived cells, 2D/3D models, organoid or co‑culture systems) to connect mechanism‑of‑action to in vivo biology and support program progression.
- Drive target discovery and validation strategies integrating human genetics and multi‑omics (e.g., bulk and single‑cell transcriptomics, proteomics, metabolomics) data in partnership with computational and data science colleagues.
- Leverage functional genomics and perturbation approaches (e.g., CRISPR/siRNA screening, AAV‑mediated gene modulation) and advanced imaging/phenotyping modalities to interrogate biology and generate causal evidence for targets.
- Define and advance translational biomarker strategies (target engagement, pharmacodynamic, patient stratification) to support preclinical‑to‑clinical translation.
- Serve as a key scientific driver or biology project lead, defining strategy, milestones, and inflection points to enable timely go/no‑go decisions.
- Collaborate in a matrix environment with chemistry, biologics, DMPK, quantitative biosciences, translational medicine, safety, clinical, commercial, and data science.
- Lead and manage external collaborations and CRO work packages, ensuring scientific rigor and delivery of objectives.
- Communicate complex data clearly through written and oral presentations to project teams, senior leadership, and governance committees.
- Mentor and/or manage scientists; uphold strong documentation, safety, and compliance standards.
- Minimum: BS degree (with 12+ years), MS degree (with 8+ years).
- Preferred:
Ph.D. (with 4+ years of relevant experience in academia and/or industry).
- Demonstrated ability to independently design, execute, and drive in vitro and in vivo studies in renal and/or cardiovascular disease biology.
- Deep, current understanding of cardiovascular, renal, and/or cardiometabolic disease pathophysiology and the ability to translate mechanistic insights into therapeutic hypotheses and testable experimental plans.
- Experience integrating human genetics and/or omics data to inform target selection, validation, and translational strategy.
- Proven leadership of discovery research programs (target discovery through robust in vivo/in vitro validation), in a matrixed, cross‑functional discovery environment with a track record of delivering decision‑grade data and advancing programs toward candidate nomination and/or IND‑enabling studies.
- Strong written and verbal scientific communication skills, including delivery of decision‑grade data to senior governance.
- Demonstrated experience in mentoring and/or managing scientists.
- Experience with functional genomics approaches such as CRISPR or siRNA…
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