Senior / Staff Data Scientist - Wastewater Target Analytics
Essential Duties and Responsibilities (What you will be doing):
- Analyze Biobot’s wastewater data, including qPCR/digital PCR, metagenomics, and mass spectrometry data.
- Identify the most important analytical problems in wastewater data. Develop novel machine learning models and algorithms to address these problems and to draw new insights from Biobot’s wastewater data.
- Execute on analyses efficiently and progressively, building team-wide conviction about results and their implications
- Turn models and analyses into prototype features/visualizations (using infrastructure such as our data warehouse), and pass these off to the product & development teams for further refinement.
Education and/or Work Experience Requirements (What you need to succeed):
- PhD in a relevant field (e.g. computer science, statistics, bioengineering, cheminformatics, computational biology). (We will also consider candidates with equivalent terminal degrees or non-PhD candidates with 5+ years of additional experience.)
- 2+ years of post-PhD experience (industry strongly preferred).
- 3+ years analyzing and visualizing biological, chemical, or epidemiological data.
- Experience in machine learning, statistical, and other data science methods for both large and small datasets, for example advanced regression, classification, and dimensionality reduction.
- Experience quickly developing new models and visualizations.
- Highly collaborative; enthusiastic about rapid prototyping and feedback on a small team.
- Proficiency in Python (or R with willingness to rapidly learn Python); experienced with version control and other good coding practices.
Education and/or Work Experience Bonuses (What will help you succeed):
- Experience analyzing multi-omics data (especially genomics and/or high-resolution mass spectrometry).
- Familiarity with public health (wastewater epidemiology is a bonus!)
- Strong oral and written communicator, able to communicate complex analyses to non-scientific audiences.
- Experience with SQL and with Snowflake or other data warehouse tools.
- Strong record of publications and/or patents.
- Experience leading projects and mentoring other team members.
- Experience with cloud-based and containerized computing (e.g. AWS, docker).
- Creativity in identifying and wrangling new data sources.