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LeadContractPythonMachine LearningData ScienceAIML
Salary Range
$165,750 - $224,250 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
We are looking for a Staff Data Scientist to join AQEMIA's Platform department. This role involves leading and scaling data science initiatives that guide decision-making in virtual screening, hit optimization, and hit-to-lead development. You will contribute across multiple projects within the Platform organization and collaborate with scientific and technical teams to define and improve AQEMIA’s data-driven approaches.
Key Responsibilities
- →Design and improve data-driven methods to benchmark and optimize in silico screening techniques.
- →Develop models and analyses to configure optimal screening workflows for each target.
- →Guide compound selection strategies through quantitative, evidence-based approaches.
- →Mentor and support data scientists within the Platform team, promoting scientific excellence and skill development.
- →Contribute to cross-functional technical strategy, working closely with engineers and ML scientists to improve tools, workflows, and model reliability across AQEMIA’s platforms.
- →Drive applied research projects in areas such as uncertainty quantification and propagation within Binding Free Energy pipelines.
- →Promote cross-department collaboration, aligning Platform’s data efforts with evolving needs in Chemistry, ML, and Engineering.
Requirements
- ✓PhD (or equivalent experience) in Data Science, Physics, Applied Mathematics, Computational Chemistry, or related fields.
- ✓10+ years of experience applying advanced data science methods to complex scientific problems.
- ✓Proven leadership in driving multi-stakeholder, high-impact scientific projects.
- ✓Strong programming skills in Python and experience with scientific computing.
- ✓Deep expertise in data analysis, statistical modeling, bayesian optimization and decision-making in R&D environments.
- ✓Strategic mindset with the ability to influence technical direction across teams.
