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Machine Learning Scientist II, Drug Discovery Analytics

Revolution Medicines
Location
Redwood City, California, United States
Posted
12 days ago
Department
Digital Drug Discovery
What they actually want (must-haves)
  • Ph.D. in machine learning, computational biology, computational chemistry, computer science, statistics, bioinformatics, or a related quantitative field; or a M.S. degree with relevant industry experience
  • Typically 2-5 years of relevant experience applying machine learning, data science, or advanced analytics to scientific datasets
  • Demonstrated experience developing, validating, and evaluating predictive or classification models
  • Strong Python programming skills and experience with scientific computing libraries such as NumPy, Pandas, and SciPy
  • Hands-on familiarity with machine-learning frameworks such as PyTorch, TensorFlow, and/or scikit-learn
  • Experience with data visualization, exploratory data analysis, and working with noisy or incomplete experimental datasets
Nice to have
  • Experience in biotechnology, pharmaceutical, healthcare, or drug discovery environments
  • Experience with phenotypic screening, high-content imaging, Cell Painting, morphological profiling, or computer vision for microscopy images
  • Familiarity with representation learning, self-supervised learning, embedding generation, dimensionality reduction, clustering, or phenotype discovery
  • Familiarity with cheminformatics or molecular modeling tools, such as RDKit or OpenEye
  • Experience with multi-omics data analysis, cloud computing environments, MLOps, or scalable model deployment
What the job really is

The Machine Learning Scientist II at Revolution Medicines will focus on accelerating drug discovery through advanced analytics and AI. This role involves developing predictive models and analytical methods to transform complex datasets into actionable insights, collaborating with various scientific teams, and contributing to a data-driven discovery ecosystem.

Benefits
  • Base pay salary range of $182,000 — $214,000 USD
  • Competitive cash compensation
  • Robust equity awards
  • Strong benefits
  • Significant learning and development opportunities.
Things to weigh
  • No specific mention of remote work flexibility beyond hybrid
  • The role requires collaboration with various scientific disciplines, which may involve complex communication and integration of diverse datasets.
  • The salary range is provided, but individual pay may vary based on multiple factors, which could lead to uncertainty in compensation expectations.
Job score3.4/5
Benefits3/5
Freshness5/5
Career value4/5
Role clarity5/5
Pay transparency0/5

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