Machine Learning Strategist

3 weeks ago


San Diego, California, United States Vertex Pharmaceuticals Full time
About the Role

We are seeking a highly skilled Machine Learning Lead to join our team at Vertex Pharmaceuticals. The successful candidate will have a PhD/MS in Computer Science, Statistics, or Computational Science with a focus on machine learning methods or a related field with 10+ years of experience.

As a key member of our Data & Methods Leadership Team, you will lead the development and application of machine learning and artificial intelligence across various scientific domains. Your expertise in classical and modern machine learning methods, including transformers, RL, GNN, and active learning methodologies, will enable you to drive strategic partnerships with other data science groups and implement novel methods into our production MLOps system.

The estimated salary for this role is $240,000 - $270,000 per year, plus benefits and bonuses based on performance. You can choose to work from home up to two days a week as part of our Hybrid-Eligible policy.

About the Job

In this critical leadership role, you will:
  • Develop and lead machine learning methods for key scientific questions across global preclinical research & pharmaceutical sciences
  • Partner with scientific experts to evaluate and develop machine learning methods in various scientific domains
  • Lead data integrity efforts to define guidelines that ensure ML/AI models have appropriate governance and documentation processes
  • Evaluate and benchmark machine-learning methods related to key scientific applications
  • Modify existing methods, or implement novel methods to address project needs
  • Collaborate with software developers to transition novel methods into the production MLOps system

About Us

Vertex Pharmaceuticals is a global biotechnology company committed to equal employment opportunity and non-discrimination for all employees and qualified applicants. We invest in scientific innovation and strive to improve the lives of patients worldwide.

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