Kimia Therapeutics is building a next-generation drug discovery engine that combines a unique physical platform for high-throughput chemistry and screening with state-of-the-art computational approaches to accelerate the discovery of novel drugs in cancer and immune disorders. We are backed by top investors in techbio and therapeutics, including Dimension and The Column Group, and are led by a veteran leadership team who have built and scaled multiple new discovery platforms and used them to put multiple drugs into the clinic. Job Summary We seek a hands-on leader for the team building the in silico components of ATLAS, spanning machine learning, data science, cheminformatics, and software engineering. Your team’s impact will span from the software foundations of ATLAS – tying our high-throughput chemistry and biology platform into a smoothly humming data engine – through the data science and machine learning directing the platform and scaling model development to accelerate drug discovery for Kimia and its partners. This is a startup role in which you will get your hands dirty with your team in building foundations from the ground up to transform how chemical optimization and modeling are performed in the industry; the desire to deliver results and learn rapidly are non-negotiable. Reporting to the CTO, you will be a hands-on leader for the team building the in silico components of ATLAS, spanning machine learning, data science, cheminformatics, and software engineering. This is a startup role, and as such it is expected that you will both manage and lead a team as well as be technically hands-on.
Responsibilities
Working with the CTO and platform leadership, identify priorities and build the digital execution roadmap aligned with company technical and discovery strategy.
Collaborate with laboratory scientists to define specific requirements for each components this roadmap
Drive execution on this roadmap spanning science and engineering, both through your team and through your own effort as needed.
Build and manage a multidisciplinary team, setting a culture of delivery, rapid systematic iteration, and high growth.
Present externally and internally in both scientific and business contexts as needed, potentially including conferences, partnering, and fundraising discussions.
Experience and Skills
BS in computer science, mathematics, chemistry or a related field. Demonstrated excellence in prior work is more critical than exact undergraduate coursework.
5+ years work experience as a senior technical contributor in two or more of software engineering, data science, and/or machine learning products; experience applying these in the context of chemistry, biology, and/or drug discovery is preferred.
3+ years of personnel management and team leadership experience, including recruiting, day-to-day management, mentoring, performance evaluation, and team strategy development.
A track record of technical leadership excellence in these fields, as evidenced by presentations, publications, or output from teams formerly led.
