Biobot’s mission is to build resilient and inclusive public health infrastructure by turning our sewers into early warning health observatories. During the COVID-19 pandemic, Biobot pioneered wastewater analysis (based on what is excreted in urine and stool) as a tool for tracking the progression of the virus through communities. We are now the leader in pathogen detection for community-level and building wastewater, as evidenced by the U.S. Department of Health and Human Services’ recent award to sponsor COVID-19 monitoring by Biobot of over 300 hundred wastewater treatment plants representing over 100M people. Since the Company’s  founding in 2017 from a Massachusetts Institute of Technology (MIT) laboratory, we have been building capabilities to combat public health crises like the opioid epidemic, and plan to expand our early warning platform to serve as a resource management hub for disease, nutrition disparities, and more. Please note that Biobot is currently unable to sponsor employment visas.

What you will be doing

• Define and grow Biobot’s data team. Establish team processes, foster a data-driven culture across the organization, and propel Biobot as a leader in data science, public health, and data for social impact.
• Develop strategic roadmaps for Biobot’s data products, identifying new opportunities for wastewater-based data to have impact and where we can leverage other existing data to add value to our unique wastewater dataset.
• Establish best practices for data science at Biobot, including tracking and sharing exploratory data analyses, versioning data, sharing insights across the organization, and communicating our work publicly to relevant communities. 
• Manage a team of data scientists, epidemiologists, and computational biologists who are developing statistical models to interpret variability in our data and extract community health insights, and coordinate analytics efforts to respond to customer feedback and questions.
• Define and drive execution against core KPIs that govern the team’s progress, measurement of impact and effectiveness against company and team goals.
• Represent the data science team and its work with Biobot’s leadership, helping set business strategy and driving high-impact decisions.

What you need to succeed

• 7+ years of relevant experience in data science, applied statistics, epidemiology, geospatial analysis, computational biology, or related. 
• 4+ years of experience in managing and mentoring data science and engineering teams in a high scale technology company. Exceptional track record as a strong and inclusive coach, providing feedback and mentoring to fellow team members.
• Experience developing algorithms and performing data analyses on complex noisy datasets, preferably with expertise in at least one of Bayesian statistics, signal processing, time series analysis, geospatial analysis and/or related concepts.
• A mastery of either Python or R, with a working knowledge of at least Python, is required.
• Demonstrated ability to communicate complex analytical and scientific concepts across a broad range of audiences, including the public, customers, company executives, and in-house technical experts.
• Ability to lead organizational change by influencing others across various functions and navigating the team through new processes and/or technical advances.
• Ability to drive complex projects from ideation to completion, ideally with formal project management experience.

What will help you succeed

• Familiarity with modern software engineering best practices, including iterative/agile development, scientific software development lifecycles, version control, and troubleshooting and testing.
• Familiarity with basic computational biology and bioinformatics concepts, including analysis of next-generation sequencing data, targeted or untargeted metabolomics, and/or other ‘omics data types.
• An understanding of regulations which govern epidemiological data systems, including requirements for data privacy, accuracy, and security. Knowledge of best practices and high-quality data sources, especially as it relates to public health and population data.