RESPONSIBILITIES

Successful candidates will:

• Utilize internal and external datasets to identify and understand the connection between disease and the epigenome. Data sources include RNA-seq, DNA methylation and standard whole genome sequencing, ChIP-seq, Hi-C, and similar.
• Apply and/or develop new algorithms and data-mining techniques for integrative analysis and visualization of genome scale data including gene expression and its relationship to epigenetic status and genome structure.
• Work in multidisciplinary teams to analyze data to design and implement new experiments and validate them using computationally assisted approaches.
• Follow coding best practices to ensure documentation and reproducibility.

SKILLS & COMPETENCIES

• Excellent analytical, problem solving and critical thinking skills, including an ability to combine attention to detail with a big picture perspective
• Strong oral and written communication skills with experience presenting scientific research across all levels of the organization
• Ability to thrive in a fast-paced, innovative, and, at times, uncertain or changing environment while remaining flexible, proactive, and resourceful
• Adept at prioritizing and managing multiple tasks simultaneously, integrating cross-functional issues, and balancing competing priorities effectively
• Collaborative and team-oriented mindset with a desire to positively impact a new company’s evolving culture
• Passionate, adaptable, and keen on working in the fast-paced biotech startup space to drive cutting-edge science

QUALIFICATIONS

• Ph.D. in Bioinformatics, Computer Science, Biology, Genetics or equivalent and a minimum of 0-3+ years of experience in computational biology and epi/genomics, or a Masters degree in Bioinformatics, Computer Science, Biology, Genetics or equivalent with 8-10+ years of experience in computational biology and epi/genomics required.
• Strong scientific background and publication record with proven high levels of performance.
• Ability to innovate, apply and develop new tools for integrative analysis and visualization of multi-dimensional genomics datasets.
• Experience with machine learning algorithms and data mining methods.
• Proficiency with Python, R or other scripting language for statistical computing and visualization.
• Ability to adapt to increasing scope and complexity of work brought on by growth/change and helps others manage through change.
• Knowledge of industry trends and ability to utilize that knowledge to determine the most efficient ways to meet business needs.