Head of Biological Data Science

Roles and Responsibilities:

• Lead the Biological Data Science department, managing a team of computational biologists, bioinformaticians, and data engineers.
• Collaborate with multidisciplinary teams of translational and pre-clinical scientists on experimental design and analytical approaches to inform preclinical R&D, therapeutic development, clinical development plans, trial designs, and trial results.
• Oversee strategic planning for developing bioinformatic analysis pipelines to support Epic Bio’s pre-clinical therapeutic and platform teams, and enable biomarker readouts for clinical trials.
• Provide expert guidance in planning, executing, and documenting computational analyses to support regulatory filings for novel therapeutic development.
• Supervise biostatistical analysis of clinical data in collaboration with the Clinical Operations team and external CROs to support clinical trial development.
• Communicate analysis results clearly and effectively to both experimental scientists and non-scientists across teams.

Required Qualifications:

• Ph.D. in Bioinformatics, Computational Biology, Genetics/Genomics, Bioengineering, Computer Science, Biostatistics, or a related field.
• 5+ years of post-PhD experience in the biotech industry, ideally in gene therapy or related next-generation drug development fields such as biologics.
• Strong analytical and scientific background with experience leading a team of scientists and managing external vendors.
• Experience analyzing high-throughput sequencing datasets (e.g., CRISPR screens, RNA-seq, ATAC-seq, ChIP-seq, WGBS, scRNA-seq).
• Experience with clinical biostatistics, clinical trial development, and/or regulatory filings in therapeutic drug development.
• Proficiency in programming with Python and R.
• Demonstrated ability to communicate research results effectively at various levels (leadership, investors, scientific experts).
• Experience in experimental design and cross-team collaboration with experimental scientists.

Preferred Qualifications:

• Expertise in gene regulation, epigenetics, CRISPR biology, and/or gene therapy.
• Theoretical knowledge and practical experience in machine learning/deep learning.
• Familiarity with protein engineering, including model-guided approaches to protein design and molecular dynamic simulations.
• Experience with regulatory submissions and interactions with health authorities, particularly the FDA.
• Familiarity with GitHub and AWS.