Key Responsibilities
- Evaluate and test virtual screening pipelines for scientific effectiveness.
- Align computational strategies with team objectives and drug discovery goals.
- Lead interdisciplinary projects across physics, chemistry, data science, and ML teams.
- Apply in-depth statistical and exploratory data analyses to improve pipeline performance.
- Identify technical gaps and develop custom solutions.
- Contribute to best practices in data analysis, coding, and screening workflows.
- Stay current on literature and recommend improvements in virtual screening.
- Advise stakeholders on the technical feasibility and impact of proposed approaches.
- Actively participate in planning analyses and identifying priorities and dependencies.
Your Profile
- Must-Have Qualifications: PhD or equivalent with 8+ years in Data Science, Physics, Computational Chemistry, or related fields.
- Must-Have Qualifications: Strong Python programming skills.
- Must-Have Qualifications: Deep experience in scientific data analysis, ideally in life sciences.
- Must-Have Qualifications: Solid understanding of statistical methods and exploratory data analysis.
- Must-Have Qualifications: Demonstrated experience leading multi-team scientific initiatives.
- Nice-to-Have: Familiarity with tools like RDKit, OpenMM, or SMILES or experience with molecular simulation data.
- Nice-to-Have: Knowledge of high-performance computing or cloud environments.
Preferred mindset
- At AQEMIA, we believe innovation thrives in the right environment. We’re looking for someone who is:
- Impact-driven & pragmatic : values results that are practical and scientifically rigorous.
- Curious & eager to learn : keen on staying at the frontier of science.
- Motivated by high challenges : enthusiastic about pushing boundaries in drug discovery.
- Team-oriented : thrives in collaborative, interdisciplinary teams.
- Humble & open : welcomes feedback and continuous growth.
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