Sr Epigenetics Computational Biologist

Overview
A biotechnology organization is seeking a (Senior) Epigenetics Bioinformatician or Computational Biologist to support research efforts focused on stem cell and ovarian cell biology. The role involves analyzing and mining genome-wide datasets-including single-cell multiomics, ATAC-seq, CUT&Tag, CUT&RUN, ChIP-seq, and EM-seq-to uncover biological insights. The successful candidate will collaborate closely with experimental biologists across a range of projects, translating computational findings into actionable hypotheses for laboratory validation.

Key responsibilities include data processing, quality control, differential expression analysis, pathway and motif analysis, regulatory network inference, and custom analyses driven by biological context. A strong ability to interpret complex, low-input datasets and communicate findings effectively to experimental teams is essential.

Responsibilities

• Independently perform project-specific analyses using both proprietary and publicly available datasets.
• Collaborate with biology teams to identify and pursue novel scientific directions based on computational insights.
• Interface with internal computational infrastructure, including databases, automated pipelines, and HPC clusters.
• Contribute to shared codebases using best practices for documentation, version control, and reproducibility.
• Stay current with literature in epigenetics, stem cell biology, and germ cell development to inform analytical approaches.

Required Qualifications

• Ph.D. in epigenetics, computational biology, genomics, systems biology, or a related field, plus 1-5 years of industry experience.
• Minimum of 5 years of experience analyzing and interpreting epigenetic datasets and translating insights into experimental strategies.
• Demonstrated leadership in epigenetics-focused projects, supported by a strong publication record.
• Proficiency in R, Python, and Bash for data analysis.
• Expertise in analyzing ATAC-seq, multiomics, ChIP-seq, CUT&Tag, and related datasets.
• Experience with version control and collaborative coding using Git.
• Proven ability to independently scope and execute complex data analyses.
• Prior experience translating computational results into testable hypotheses and experimental designs.
• Strong communication skills, with the ability to engage diverse scientific audiences.
• Experience working with high-performance computing clusters and/or cloud computing environments.

Preferred Qualifications

• Experience with induced pluripotent stem cell (iPSC) derivation and germ cell biology.
• Familiarity with relevant bench techniques.
• Experience applying deep learning tools to multiomics datasets.