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Drug Discovery Data Science Jobs (NOW HIRING)

Corteva, Inc.

The successful candidate will join a strong, globally distributed Data Science team applying advanced analytics and AI to accelerate biologicals discovery - spanning both microbial and natural ...

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How much do drug discovery data science jobs pay per year?

As of Sep 15, 2026, the average yearly pay for drug discovery data science in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

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Infographic showing various Drug Discovery Data Science job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Senior Machine Learning Scientist II, Drug Discovery Analytics

Redwood City, CA • On-site

Revolution Medicines
See more jobs at Revolution Medicinesexternal

Full-time

Re-posted 25 days ago

Job description

The Opportunity:

  • We are seeking a Senior Machine Learning Scientist to help accelerate drug discovery through advanced analytics and artificial intelligence. This role will develop predictive models and analytical methods that transform complex biological and chemical datasets into actionable insights that guide research decisions.

  • The Senior Machine Learning Scientist will work at the interface of data science, chemistry, and biology to support target discovery, compound optimization, and translational research. This position requires both strong machine learning expertise and the ability to collaborate effectively with experimental scientists to solve real-world scientific problems.

  • The successful candidate will contribute to building a data-driven discovery ecosystem where data, analytics, and experimentation continuously inform and accelerate one another.

Key responsibilities include:

  • Develop Predictive Models for Drug Discovery

  • Independently Design and implement machine learning models to predict compound activity, selectivity, and developability.

  • Identify and Develop predictive frameworks for ADME/Tox, target engagement, and phenotypic screening outcomes.

  • Apply advanced modeling approaches including deep learning, graph neural networks, and ensemble methods.

  • Evaluate model performance and apply appropriate validation strategies.

  • Work with data engineers and ML engineers to integrate models into discovery pipelines.

  • Analyze Complex Scientific Data.

  • Perform exploratory data analysis on chemical, biological, and phenotypic datasets.

  • Integrate heterogeneous datasets including:

  • Chemical structure and screening data.

  • Structural biology and molecular simulation outputs.

  • Collaborate with Research Scientists.

  • Partner with medicinal chemists to support compound design and lead optimization.

  • Work with biologists to interpret experimental results and identify new target opportunities.

  • Translate scientific questions into computational modeling strategies.

Required Skills, Experience and Education:

  • PhD in machine learning, computational biology, computational chemistry, computer science, statistics, or a related quantitative field.

  • 6-10 years experience applying machine learning or advanced analytics to scientific datasets.

  • Python and scientific computing libraries (NumPy, Pandas, SciPy).

  • Machine learning frameworks (PyTorch, TensorFlow, scikit-learn).

  • Model development, validation, and evaluation methods.

  • Data visualization and exploratory analysis.

  • Experience working with noisy and incomplete experimental datasets.

Preferred Skills:

  • Cheminformatics or molecular modeling tools (RDKit, OpenEye, etc.).

  • Multi-omics data analysis.

  • Cloud computing environments.

  • MLOps or scalable model deployment.

Apply