Job:

Scientist, ChemInformatics & Machine Learning - 00054

Department:

Data Science

Location:

Mystic, CT USA

Date:

May 21, 2024

At Enko we believe that to create the future the world wants, we need to change the way the world grows. Our goal is not just to create something better, but to play a part in something bigger. Enko discovers and develops novel products for farmers to protect their crops from pests and disease.

• Our products address a $70 billion core market focused on crop health with expansion into adjacent markets worth $300+ billion.
• We have $150 million in capitalization, with $80 million raised as part of a Series C in December 2022.
• Our global investors include Anterra Capital, Nufarm, the Bill & Melinda Gates Foundation, Eight Roads Ventures, Finistere Ventures, Rabo Food & Agri Innovation Fund, Novalis LifeSciences, Syngenta, Taher Gozal, Germin8 Ventures, TO Ventures Food, Endeavor8, Connecticut Innovations, Silver Blue LLC, and Alumni Ventures Group.
• We have over 40 novel pipeline programs and 5 strategic partnerships announced, including Syngenta and Bayer
• Our headquarters and research facility in Mystic, CT has 90,000 square feet of state-of-the-art labs and computer-controlled greenhouses built for pre-field experimentation and scale.

Our agricultural invention platform, Enkompass™, uses A.I. and machine learning to create more effective ways of finding and selecting the right treatments for the right targets. Led by a team of proven scientists, entrepreneurs, and industry veterans, Enko’s innovative science, agile design, and discovery of new modes of action is producing next-generation crop protection solutions that will overcome the critical challenges facing industry. The industry has taken notice, and we have active projects with many key industry players…with more to come! We are hiring across the organization to achieve our desired growth objectives, and our best asset is our team members. We value you and your career journey. We focus on impact over hours!

• Masters/Ph.D. in a relevant scientific discipline (e.g. Chemistry, Biology, Computational Science, Statistics) with experience in application of cheminformatics and machine learning for small molecule discovery. Experience in an industry setting and experience with DNA encoded library data are desired.
• Strong analytics skills with experience analyzing and interpreting biological assay data (e.g. in vitro assays, bioassays, etc.)
• Highly proficient in developing repeatable and scalable analysis pipelines using Python in a professional environment; Experience with infrastructure tools, analysis tools, and machine learning tools is preferred.
• Ability to learn and adapt to new software tools, with experience in computational chemistry software a plus.
• Demonstrated ability to drive projects from conception through development, testing, and deployment.
• Proven ability to effectively communicate complex technical concepts to both technical and non-technical audiences.

We are seeking a strong scientist who can apply their expertise in cheminformatics to the discovery and development of our products. The ideal candidate is passionate about collaboration within and across scientific disciplines while building rigorous, reproducible scientific workflows using Python, AWS, and other core Cheminformatics and Data Science tools. We are actively growing so there is ample opportunity to both build upon our established base, and to drive new ideas.

• Support discovery, hit to lead, and lead optimization efforts across multiple projects through expert cheminformatics analysis.
• AnalyzeDNA encoded library screening data and prepare these data for project teams to make compound selection decisions.
• Build and validate machine learned models and collaborate with project teams to apply these models.
• Build and validate tools for generating new molecules based on known inputs (e.g.enumeration, generative AI, etc.)
• Build and scale automated pipelines for data preparation, cleaning, training, inference, etc.
• Develop tools, dashboards, and presentations that enable scientists to capitalize on insights derived from data.

Masters/Ph.D. in a relevant scientific discipline (e.g. Chemistry, Biology, Computational Science, Statistics) with experience in application of chem informatics and machine learning for small molecule discovery. Experience in an industry setting and experience with DNA encoded library data are desired.

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