Life Science People has partnered with a client-focused on reshaping healthcare by developing new approaches to measure, monitor, and treat age-related diseases. This endeavor builds on extensive research into blood-based molecular signatures of individual organs to understand their changes with age and disease. The work involves exploring proteomics, genomics, and electronic health records to unlock new treatments and preventative strategies for age-related conditions. A background in aging or neuroscience is beneficial for this role.
The successful candidate will collaborate closely with a diverse team, including scientific founders, machine learning experts, and engineers. They will be pivotal in scaling the company's 'omics' analytics capabilities and developing pipelines and databases. As this is a lean startup, the candidate must bring a hands-on approach, be proficient in writing production-level code, and have experience with machine learning tools.
Responsibilities
Spearhead the development and deployment of computational target prioritization pipelines. Lead the acquisition, integration, analysis, and interpretation of multi-omics and clinical data to assess disease risk and progression. Collaborate on the development and productionization of analysis pipelines, automated workflows, and databases ensuring strong data provenance. Proactively identify challenges and innovate solutions autonomously. Clearly interpret data and formulate strategies for computational and biological validation. Collaborate with team members and scientific advisors to enhance analytics capabilities. Stay updated with the latest data science and computational biology research and methodologies, integrating these into internal R&D.
Qualifications
Minimum 5-year industry experience in multi-omics data analysis.PhD (or equivalent): Computational Biology, Bioinformatics, Quantitative Genetics, Systems Biology, or Neuroscience. Strong quantitative reasoning and statistical analysis skills with experience in network-based systems biology. Proficiency in programming (R and Python), developing testing frameworks, and creating data processing pipelines. Proficiency in using and integrating data from UKBB and other databases such as the Human Protein Atlas, Drug-Bank, DisGeNET, STRING, BioGRID, and other similar top databases in network biology and systems pharmacology. Hands-on coding and technical work. Preferred: Drug discovery experience, CNS/neurodegenerative disease research, machine learning applications in biological data.
Logistics
Completely Remote Competitive Salary Strong Equity Healthcare Benefits Unlimited PTO
