Boost is looking for an outstanding candidate able to bring their combined experience in data analytics and microbiology to the design, build, and expansion of our microbiome analytics system, including storage and analysis, that can function across multiple host microbiome systems. You will be able to implement new and innovative methods for identifying and interpreting species-level interactions within a microbial community.
The role will contribute to the development of code, bioinformatics analyses pipelines, computational tools, and relational/graph databases for mining and visualizing large microbial data sets as well as identify opportunities to extend and improve existing methods for network analysis by introducing novel approaches, including via machine learning models. Collectively this will enable Boost to fully realize the potential of its Interactome Discovery Platform and turn it from concept to product pipeline with efficient, high throughput analysis enabling clear decision-making.
Who You Are
You have expertise in and are passionate about microbiome research and their manipulation for product discovery. You will appreciate that valuable analysis is accompanied by actionable outcomes that can be leveraged by the Platform and Product Teams to accelerate strain discovery and product development. Through the use of mathematical and statistical models you are able deliver insight, recommendations, and solutions. This will enable you to provide analysis and feedback about experimental results to supervisors, highlighting important results and defining next step experiments.
Boost seeks a person who can demonstrate knowledge of specialized scientific techniques relative to their area of expertise and coordinate and cooperate on research activities with peers and supervisors. You are someone who can communicate effectively through listening, documentation, and presentation and have a broad understanding of instrumentation and scientific principles. You are comfortable with and have experience providing oral and written reports on experimental results in order to enable stakeholders to make appropriate decisions based on scientific results.
You are excited by the opportunity to work in a fast-paced start-up environment with many moving parts. Agility is one of your key attributes and you are able to react and trouble-shoot in real time.
· Proficient analyzing microbiome data, with environmental microbiomes preferred, including 16S taxonomic profiling and metagenomic binning and functional annotation
· Experience analyzing WGS data is highly desired
· Strong experience programming in R; Python is a plus - shell scripting required, and familiarity with SnakeMake preferred
· Proficient with AWS and Git version control
· Strong experience developing robust and reproducible analyses for microbiome data and experience building bioinformatics pipelines to scale analytics
· Comfortable with current bioinformatics tools and databases for interpretation of microbiome sequencing data
· Experience designing and constructing large scale databases (relational, graph, etc.)
· Familiarity with machine learning algorithm development and applications
· Must be able to collaborate effectively with Molecular Microbiology team to support rigorous experimental design and propose innovative downstream bioinformatic analyses.
· Strong organizational and record keeping skills
· Broad understanding of scientific principles with an ability to conceptualize, design, and execute experiments that address research questions
· Proficiency with experimental design and statistical analysis
· Proven ability to handle large genomic datasets efficiently using scripts, databases, and other computational tools
· Excellent verbal and written communication skills, able to prioritize and execute tasks in a complex and agile environment, and drive results within set timeframes
· Commitment to self-development and to learning new tools, techniques, and skills
· Recent PhD or Master's degree with three years industry experience in Computational Biology, Microbiology, Bioinformatics, Genomics, Microbial Ecology, Computer Science or related field with one or more years of relevant experience
· Experience working in an industry setting preferable