Data Scientist/Computational Biologist
Indigo is a company dedicated to harnessing nature to help farmers sustainably feed the planet. With a vision of creating a world where farming is an economically desirable and accessible profession, Indigo works alongside its growers to apply natural approaches, conserve resources for future generations, and grow healthy food for all. Utilizing beneficial plant microbes to improve crop health and productivity, Indigo’s portfolio is focused on cotton, wheat, barley, corn, soybeans, and rice. The company, founded by Flagship Pioneering, is headquartered in Boston, MA, with additional offices in Memphis, TN, Research Triangle Park, NC, Sydney, Australia, Buenos Aires, Argentina, and São Paulo, Brazil. www.indigoag.com
The Data Scientist/Computational Biologist will drive innovation throughout R+D by turning laboratory assay, growth room, greenhouse, and field trial data into actionable insights. As a member of the machine learning team, they will design, denoise, and analyze experiments to find predictors of field success. The ideal candidate will be an excellent communicator, who enjoys working with laboratory scientists, quantifying their work and extracting as much information as possible using tools of modern data science and machine learning. The data scientist will have a unique opportunity to drive innovation and discoveries throughout Indigo. We desire a a quick and flexible thinker, who is always looking for the next way to connect disparate data and to implement the best recent technical advances to advance Indigo’s R+D pipeline.
- Embed within the discovery team to design, denoise, and analyze sophisticated, high throughput assays for both abiotic and biotic stresses
- Work with the greenhouse and growth room teams to design and analyze soil based screening assays and product formulation and positioning experiments
- Assist with the analyses of complex field trial data including understanding potential interaction with environment, genotype, and natural microbiome
- Develop strategies to connect data at all stages of the R+D pipeline and use machine learning to speed and improve the quality of results
- Analysis of quantitative experimental data, especially in the biological sciences.
- Design of scientific experiments, e.g. power analyses, handling technical and biological variation, etc.
- Image processing and analysis
- Statistical and probabilistic modelling of data
- Machine learning techniques for feature selection and exploratory analyses (e.g. clustering, LDA, etc.)
- Writing reusable, comprehensible software that can be productionized
- Presenting technically sophisticated analyses to audiences at disparate levels of sophistication
- Strong desire to continue learning, identify new techniques and technologies, and rapidly implement them to keep Indigo at the cutting edge (e.g. reading bioRxiv digests daily and testing new tools)
- Desire to support machine learning with assay data
- Agile with the ability to deliver in a fast-paced environment
- Ability to understand needs of customers, especially laboratory scientists, and experience working directly with stakeholders to implement exactly what the customer needs
- Desire to teach quantitative skills to laboratory scientists and continually increase the level of sophistication in experimental design and analysis
- Team player, excellent communication skills
- Passion for Indigo and our core values
- PhD in Computational Biology or Other Quantitative scientific/engineering discipline (e.g. Physics, Biology with computational research) or Masters degree with 3+ years experience
- At least 3-5 years' experience with programming in Python and/or R
- Understanding of and experience working with biological data
Indigo is committed to living our values, specifically “creating a work environment where everyone feels respected, connected, and has opportunities to learn and grow.” As part of living our values, we strive to create a diverse and inclusive work environment where everyone feels they can be themselves and has an equal opportunity of succeeding.