Machine Learning Scientist II
New
R
Revolution MedicinesOncology Drug Discovery
Remote (United States)Full-TimeMiddle
Salary182,000 - 214,000 USD per year
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Job Details
- Experience
- Typically 2-5 years of relevant experience
- Required Skills
- PythonMachine LearningNumpyPyTorchData sciencePandasTensorflowscikit-learn
Requirements
- Ph.D. in machine learning, computational biology, computational chemistry, computer science, statistics, bioinformatics, or a related quantitative field.
- 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.
- 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 and exploratory data analysis.
- Experience working with noisy or incomplete experimental datasets.
- Ability to communicate technical work clearly and collaborate effectively with cross-functional scientific partners.
Responsibilities
- Develop, implement, and evaluate machine-learning models supporting drug discovery, including compound activity, selectivity, developability, and target engagement.
- Perform exploratory data analysis and quality assessment on chemical, biological, imaging, and phenotypic datasets.
- Integrate heterogeneous datasets such as chemical structures, screening data, molecular simulations, and high-content imaging outputs.
- Apply supervised learning, deep learning, graph-based, and ensemble methods for scientific research.
- Collaborate with data engineering and ML engineering partners to build reproducible workflows.
- Partner with medicinal chemists and biologists to translate scientific questions into computational analyses.
- Document methods, code, and results to support reproducibility and knowledge sharing.
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