is a joint venture between Bayer and Ginkgo BioWorks dedicated to addressing unmet needs in agriculture by applying synthetic biology approaches to engineer microbes.
- Lead data analytics group to implement visual data analytics, present findings to internal stakeholders, scale ad-hoc analyses into prototype level analytics pipelines. Collaborate closely with statistical and data scientists, and with technical functions, CROs, and program teams to help build engineered microbial products for agriculture.
- Visual Insights and Data Storytelling: Apply modern data science and biostatistics tools for design and analysis of lab experiments to evaluate and rank biological performance of engineered microbes. Structure and simplify complex scientific questions and approaches to link analytics strategy and outcomes datasets. Communicate effectively through listening, documentation and presentations, especially using compelling visualizations to share analysis and interpretation of data.
- Experimental Design, Assay Development, and Assay Quality Analytics: Create novel scientific insights and starting points by analyzing plant and lab data from multiple in vitro experiments and instruments, including but not limited to high throughput formats, colorimetric spectroscopy, protein quantification assays, LC/MS, growth curves, microscopy, etc. Seek opportunities to improve methods, operations, and analyses by application of statistical or quantitative tools. Develop ad-hoc analyses into standardized and automated analysis pipelines. Build and maintain a scientific code base, developing QC and analytic pipelines.
- Drive a culture of data-driven insights: Build relationships across the R&D departments and CROs including Ginkgo Bioworks, and build consensus based on data-driven insights. Maintain a credible record of unbiased truth. Lead cross-functional data science teams to meet program timelines. Contribute to increasing the data fluency in Joyn Bio by mentoring others in data analysis concepts. Inspire others with new approaches, your way of thinking, and the potential for data science to accelerate their work.
- M.S. or Ph.D. with a scientific focus – some examples include but are not limited to Biology, Microbiology, Chemical Biology, Biological Engineering, Plant Biology, etc with interest and development in data analytics and biostatistics.
- Multiple years of industry experience (5+ years) and team management with a player coach mindset.
- Expertise in data visualization and data storytelling, presentation skills
- Strong project leadership and communication skills, collaborative mindset and track record with cross-disciplinary functional teams.
- Build relationships across the R&D departments and CROs, and build consensus based on data-driven insights.
- Experience with high throughput bioassays development and screening experiment design.
- Analytical skills and mindset to solve real-world problems with applied data science; enjoys structuring and simplifying complex problems.
- Fluent in Python, R, and capable of framing problems as programming tasks; command line usage.
- Experience with JMP, Spotfire, R Shiny/Dash/Plotly/Kibana or other web-based interfaces to create graphic-rich data explorations.
- Familiarity and experience with modern data science tech stack and concepts (e.g. AWS, Git, Agile, etc).
- Experience with interfacing via APIs with multiple database types, including SQL and NoSQL.
- A passion for creating transformative agricultural products, discovering new ways to add value to farmers.
LocationThis position is based in Boston, Massachusetts and may accommodate flexible working arrangements.
- This position reports to the Data Science Lead.
- Joyn Bio welcomes applications from all individuals, regardless of race, national origin, gender, age, physical characteristics, social origin, disability, union membership, religion, family status, pregnancy, sexual orientation, gender identity, gender expression or any unlawful criterion under applicable law. We are committed to treating all applicants fairly and avoiding discrimination.