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Salary: USD 130,000 - 165,000 / annual
NewLimit is a biotechnology company developing medicines to treat age-related diseases by reprogramming the epigenome. The company uses functional genomics, pooled perturbation screening, and machine learning to understand epigenetic aging and disease.
The Metabolism team is developing reprogramming medicines to restore youthful function in aged hepatocytes in the context of metabolic disease. As a Scientist on this team, you will be a key contributor to therapeutic development efforts, serving as a subject matter expert in liver biology and metabolism.
Key responsibilities:
- Collaborate with Single Cell Technology, Epigenetic Editing, and Predictive Modeling teams to design, build, and test epigenetic reprogramming interventions for aged and diseased hepatocytes
- Develop assays using preclinical models of liver disease to measure hepatocyte function
- Evaluate epigenetic reprogramming payloads using in vivo functional assays to select therapeutic candidates
- Deliver epigenetic reprogramming perturbations to hepatocytes in vivo using multiple transgenic manipulation methods (e.g., viral vectors, lipid nanoparticles)
- Analyze and interpret experimental data to drive pre-clinical decision making
- Contribute to team decision making as a subject matter expert
Requirements:
- Ph.D. in cell biology, molecular biology, biochemistry, or a related field, OR equivalent industry experience (5+ years)
- Experience with primary liver cell isolation and functional characterization
- Experience working with mouse models of liver dysfunction
- Experience with transgenic manipulation of liver cells either ex vivo or in vivo (e.g., using viral vectors, nucleic acid delivery)
- Exceptional organizational skills, including detailed experimental record-keeping and the ability to manage multiple concurrent projects
Nice to have:
- History of contributing to a drug development program at the pre-clinical or clinical development stage
- Experience leading the design and execution of pre-clinical studies in mouse models
- Experience with single cell genomics methods
- Experience with pooled screening approaches using bulk or single cell read-outs (e.g., CRISPR fitness screens, Perturb/CROP-seq)
