About the job
We are looking for an expert in ML method development for biological data analysis, in domains such as network analysis, systems biology, graph-based modeling, causal structure learning, single cell omics, or imaging modalities. Your expertise will help the team navigate the complexities of developing disease relevant cell models and analyzing high throughput phenotypic screens, and ensuring that the tools being developed are calibrated and effective, and that analyses are performed to the highest rigor and in line with best practices in the broader scientific community.
Responsibilities
Collaborate closely with experimental biologists, computational biologists, and other machine learning scientists, support the identification of novel phenotypes, the development of new screening paradigms, and advance our understanding of disease. Develop and utilize diverse machine learning and bioinformatic methods to perform diverse downstream analyses, including integrating with other data modalities, including human cohort data in order to extract insights about disease mechanisms. Be part of a cross-functional team of life scientists, data scientists, bioengineers, software engineers, and machine learning scientists that strive to identify therapeutic targets and develop drugs of high efficacy and low toxicity.
Qualifications
Minimum
Ph.D. in computer science, machine learning, computational biology, systems biology, or a related discipline. Extensive hands on experience developing ML methods for biological data modalities. Hands on experience with biological data analysis, in particular familiarity with network and graph based analysis and modeling techniques. Experience integrating data or insights from multiple sources and distinct modalities (e.g., imaging, transcriptomics, functional genomics, genetics, human cohort data). Strong programming skills in scientific programming languages (i.e., Python). Committed to writing well-commented code and documentation, and familiarity with coding best practices (i.e. version control, code review). Ability to communicate effectively and collaborate with people of diverse backgrounds and job functions. Publication record of meaningful contributions to high-quality work in relevant machine learning, computational biology, systems biology, life sciences, or biomedical venues. Passion for developing useful and impactful methods and making a difference in the world.
Preferred
Experience with statistical genetics and integrating functional and omics data with gwas. First-hand experience studying disease biology. Passionate about problem solving, asking questions and learning independently. Experience with gene regulatory network inference or causal modeling. Familiarity with cloud computing services (e.g., AWS or azure). Demonstrated ability to write software in a team, industry experience or substantial involvement with open source projects. Experience building infrastructure for data processing.