San Francisco Bay Area
Brightseed believes in a new health paradigm where nutrition is medicine. A paradigm informed by advances in genomics, bioinformatics, and the health sciences, resulting in solutions that reduce risk and ameliorate consequences of chronic disease. Brightseed's reason for being is to naturally improve the quality of life. To enable this mission, we developed the Forager(TM) discovery platform, which combines machine learning and systems biology to discover naturally occurring compounds with proven clinical benefits for health and wellness.
Brightseed discoveries unleash the power of natural molecules in everyday food, as well as provide novel targets for therapeutic development. Magellan, our first major discovery, helps restore metabolic health and combat the adverse consequences of obesity. Our discoveries will be commercialized as nutritional products, food ingredients and medical foods.
At Brightseed, we are committed to achieving transformative results with a team-first mentality. Above all, we seek team members who are truth-seeking and trusting. This means we hold ourselves and each other accountable in our quest for groundbreaking knowledge. We are also a highly collaborative team that prioritizes shared goals over individual tasks. As such, we share workload, information and feedback generously and look out for each other's wellbeing. Ultimately, we realize that achieving our purpose of improving lives through nutrition will take our collective intelligence and efforts.
Our founders have a distinguished 20-year plus track record in scientific research and translation of discoveries into commercially successful products. We have also assembled an accomplished network of scientific and business advisors that actively engages our team as thought partners, problem solving resources, and mentors. We are supported by a strong, aligned investor base with deep capital and a history of growing successful enterprises.
You are an expert bioinformatics scientist with a passion for exploring the chemical diversity encoded in organismal genomes and how that diversity can be harnessed for the betterment of human health. At Brightseed, you will be responsible for building out an innovative computational platform that integrates machine learning, metabolomics technologies, and high-throughput screening data, to elucidate the natural products space found in major domains of life, including plants, fungi and microbes. As part of your work, you will build-out our internal capabilities to perform metabolomics data analysis in collaboration with our chemistry team. Ultimately, your work will result in a natural compound library and a data-mining toolkit as a crucial part of our Forager (TM) platform to make novel discoveries.
In particular, you will:
Design, build, and operate data pipelines that perform core metabolic computational analyses.
Design and perform computational investigations that lead to natural product discoveries.
Develop and train algorithms across a range of learning objectives, including molecular structure and function prediction, bio-safety inference, and phylogenetic profiling, among others.
Capture, curate and manage data sets, including creating data analysis tools and databases.
Build and maintain the R&D computational infrastructure, including apps, pipelines, servers, databases, and cloud services.
Integrate with the in-house chemistry team to build a seamless team spanning multiple scientific disciplines
During the first year at Brightseed, we would expect you to:
Develop algorithms to analyze and interpret untargeted small molecule metabolomics data generated by LC/MS and MS/MS platforms.
Build a searchable natural compound library from public and proprietary metabolomics data sets.
Establish a computational framework to screen for natural products with desired bioactivity and biosafety properties
Ph.D. in Bioinformatics, Cheminformatics, Computational Biology, Biostatistics, Biophysics, Chemistry, Computer Science, or related discipline.
Proven expertise applying computational/statistical/machine learning approaches to biological investigations.
Experience handling and analyzing untargeted metabolomics and mass spectra data is highly desirable.
Additional experience with one or more of the following is a plus: Statistics, Molecular Biology, Systems Biology, Natural Product Chemistry, Organic Synthesis, Next Generation Sequencing
Strong oral and written skills
R and Python skills preferred