Director, Discovery Bioinformatics Oncology

Eli Lilly

Base: $193,500 - $283,800; bonus: compyny bonus de...
Not specified
Phd in computer science or related stem field
5+ years ml experience in biotech/pharma
Deep learning expertise for biological problems
Eli Lilly is seeking a Director of Discovery Bioinformatics in Oncology to lead AI and machine learning innovations aimed at advancing oncology drug discovery. The ideal candidate will have extensive experience in bioinformatics, deep learning, and leading high-performing teams, with a strong focus on integrating diverse biological data for actionable insights

Job Summary

  • This role leads the AI/ML strategy to accelerate oncology drug discovery through advanced machine learning and deep learning architectures.
  • The successful candidate will partner with biology and chemistry teams to transform heterogeneous molecular data into actionable hypotheses and decision-quality insights.
  • Lilly offers a comprehensive benefit program including medical, dental, vision, 401(k) matching, and flexible spending accounts alongside a competitive salary range.

Matching Summary

Match Score: 85

Eli Lilly is seeking a Director of Discovery Bioinformatics in Oncology to lead AI and machine learning innovations aimed at advancing oncology drug discovery. The ideal candidate will have extensive experience in bioinformatics, deep learning, and leading high-performing teams, with a strong focus on integrating diverse biological data for actionable insights.

Salary

Base: $193,500 - $283,800; Bonus: Company bonus depending on performance; Benefits: Comprehensive medical, dental, vision, 401(k), and wellness programs

Skills & Requirements

Must-have

  • PhD in Computer Science or related STEM field
  • 5+ years ML experience in biotech/pharma
  • Deep learning expertise for biological problems
  • PyTorch or JAX/TensorFlow framework proficiency
  • MLOps with Docker, Kubernetes, and AWS
  • Experience with transformer-based sequence models
  • Leadership of cross-functional scientific teams

Nice-to-have

  • Experience with Hugging Face foundation models
  • Background in active-learning loops
  • Publications or patents in computational biology
  • Knowledge of physics-informed constraints
  • Familiarity with RAG and agentic workflows

Key Requirements

  • PhD in Computer Science, Computational Biology, Bioinformatics, Statistics, or Applied Math
  • Minimum 5 years post-doctoral or industry experience delivering ML solutions
  • Proven track record applying deep learning to protein/antibody design

Work Rights

Not specified

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