Associate Director – Computational Biology / Machine Learning

Associate Director – Computational Biology / Machine Learning

Requirements

• PhD in Computer Science, Computational biology/Bioinformatics, Mathematics, Statistics, or EE
• 5+ years’ experience with a wide variety of classical and modern methods used in machine learning and AI (unsupervised, semi-supervised, or supervised learning) as evidenced by strong publication record and/or algorithms released into production
• Strong grasp of veridical data science, explainable AI, scientific measurement error, sources of variation, and best practices to control variation either experimentally or algorithmically
• 5+ years of industry experience in ML including at least 3 years’ experience in biotech, pharma, or diagnostics with increasing technical or managerial responsibility in a research or product development context
• 5+ years expertise programming in Python/R including modern ML frameworks (Tensorflow, Pytorch, or Jax), production compute environments (AWS/Nvidia), and versioning principles for reproducible model-building and evaluation
• Experience developing research-grade algorithms based on biological data, and collaborating with software teams to prioritize and move research efforts move to production
• 3+ of experience managing a team in a fast-paced environment with proven success delivering statistical models, machine learning algorithms and results in pursuit of corporate objectives
• Excellent mentor and manager with demonstrated ability to develop the career of junior scientists and engineers to inspire their best work
• Collaborative, passionate, and driven with a “can-do” attitude

Responsibilities

• Manage and lead a team of data scientists developing and locking computational algorithms to detect cancer from blood-based omics assays
• Oversee day to day technical work of the team, bringing to bear best practices from statistical / machine learning
• Read and review the literature, code releases, patents, and hardware/systems updates to keep abreast of current advancements in the fast-moving field of computational biology & ML
• Develop solid understanding of in-house assays and workflows, sources of variation, and data pipelines
• Collaborate closely with peers in assay development, clinical data science, and software to advance performant models from research into a production environment

Preferred Qualifications

No preferred qualifications provided.