VP, Machine Learning

FL94 is seeking a hands-on,n experienced, creative, and visionary Machine Learning Lead. You will be responsible for setting the strategy for, building, and leading a team of data infrastructure and machine learning scientists. The objective of this team is to develop and apply cutting-edge machine learning (ML) paradigms across FL94’s proprietary chemoproteomics and small molecule chemistry data sets to narrow the search space and increase the probability of discovering novel chemistries via generative design.

This position is ideal for someone with deep expertise in machine learning, deep learning (particularly for building biologically meaningful representations of chemical structures), and computational chemistry, sciences and who is committed to building a high-preforming team.

Successful candidates will be able to thrive in a fast-paced, action-oriented, team environment and display a clear passion for science and impact. The role offers a unique opportunity to help drive the strategy of an early stage, big concept biotech. The candidate must be an entrepreneurial, highly motivated and purpose-driven leader who is able to strategically build high-performing teams. The successful candidate will have a track record of accomplishments in data science/machine learning as well as demonstrated high-impact research in academia or industry.

S(he) must exhibit excellent leadership, project management, and strong collaboration and communication skills. S(he) should be ready to champion new ideas and lead breakthrough method development in a very dynamic and innovative environment.

Key Responsibilities:

• Develop the vision and strategic plan for FL94’s machine learning platform, driving novel discoveries in small molecule chemistry.
• Build and lead the data science team translating small molecule and proteomic drug discovery challenges into breakthroughs via deep learning applied to computational chemistry.
• Own the philosophy and strategy behind core machine learning technology and applications.
• Develop a robust data portal that will enable the application of generative models to proprietary chemoproteomics data.
• Define team mission and propose org structure and interaction model to ensure for successful execution.
• Oversee the development and prototyping of novel ML algorithms for drug discovery problems.
• Ensure effective training and validation of large deep learning models at scale.
• Identify and act upon new areas of applied research that can further the platform’s ability to have therapeutic impact.
• Work collaboratively in cross-functional chemistry, proteomics, drug discovery and AI/machine learning teams.
• Partner across the organization to align data generation with infrastructure, innovative approaches, and data integration strategies.
• Communicate strategy, R&D progress to leadership & actively engage in outreach to external advisors.
• Prepare and deliver scientific updates to the executive leadership team, board of directors, and company advisors.
• Assemble and manage machine learning and data science Scientific Advisory Boards.
• Embody FL94’s core mission to enable generative design of protein editing chemistries and live and set a culture that is highly resilient, optimistic, innovative, solutions-oriented, transparent, and inclusive.

Qualifications:

• A proven leader with a Ph.D. in machine learning, statistics, computer science, mathematical modeling, operations research, or related field computational chemistry, or other highly-technical fields from a recognized higher-education establishment.
• >3-10 years of experience in the area of computational science and artificial intelligence with track record and experience track record of solving real-world problems. Ideally candidates would have deep experience in small molecule drug discovery, talented candidates without such experience will also be considered.
• Demonstrated mastery of a broad array machine learning and deep learning approaches across a variety of deep learning architectures, deep learning libraries, and cloud-computing development environments, with an emphasis on ML techniques for studying small molecule drug development (particular attention…