Director, Data Science and Machine Learning

Responsibilities

• Pioneer and lead computational efforts for FL121’s platform science
• Develop a data framework and database for tracking DNA translation to chemistry and chemistry to drug characteristics
• Strategically map out chemical representation optionality and develop a scalable framework for platform chemistry
• Lead base-case chemical prediction models and forecast data needs to ensure strong predictive power
• Build and support computational capabilities to unlock de-novo mass spec deconvolution models
• Integrate computational efforts across all platform workstreams to create a cohesive data-collection workplace
• Serve as a cross-functional program lead, guiding interdisciplinary teams of computational scientists, medicinal chemists, and synthetic biologists
• Communicate computational goals, progress, and key decisions clearly to both scientific and executive audiences
• Build and manage budgets, allocating in-house and external resources efficiently
• Foster a collaborative, psychologically safe, and people-first team environment

Requirements

• Masters or PhD in a relevant field (e.g., computational chemistry, machine learning, or data science)
• 10+ years of experience in scientific, engineering, or computational settings within biotechnology
• Proficiency in Python and modern ML frameworks
• Hands-on experience with tools such as RDKit, scikit-learn, or Chemprop
• Experience with informatics software and platforms including AWS, Seqera, or Benchling
• Proven track record of leading complex, cross-functional scientific programs
• Strong emotional intelligence and ability to mentor PhD and non-PhD scientists
• Exceptional communication skills for conveying complex ideas to diverse stakeholders

About the Company

FL121 is a privately held biotechnology company engineering nature to build next-generation peptide therapeutics. By harnessing naturally occurring chemistry and pairing it with sophisticated high-throughput screening technology, we unlock the future of de novo drug design via curated data creation and model training.