Senior Data Scientist refactoring Python optimization algorithms and developing pricing models. Solving supply - chain challenges for Tiger Analytics’ retail and CPG analytics clients.
About the role
- Staff ML Research Scientist focusing on deep learning for drug discovery. Join SandboxAQ’s innovative team tackling global challenges through AI and collaborative research.
Responsibilities
- Drive the research and development of next-generation deep learning models for protein-ligand co-folding and affinity prediction.
- Bridge from research to commercial utility by equipping SandboxAQ’s software products with advanced predictive capabilities.
- Bring novel ideas and the content of scientific papers into the ideation, training, and benchmarking of complex models, ensuring they are optimized for large-scale, real-world drug discovery applications.
- Act as a technical beacon for the team, representing SandboxAQ scientifically and shaping its vision externally and internally.
- Mentor junior researchers and collaborate across engineering and product teams to foster a culture of technical rigor and rapid iteration.
Requirements
- PhD in Computer Science, Computational Chemistry, or a related field, with specific focus on structure-based deep learned affinity modelling a plus.
- At least 4 years of post-PhD experience, including experience in a professional industry setting, with a track record of delivering scientific impact that translates to product.
- Direct, hands-on experience developing and executing leading-edge co-folding and/or affinity prediction models, from proof of concept to productionized workflows.
- Proven excellence in co-folding and/or affinity prediction, as demonstrated by participation in industrial projects and/or academic publications.
- Experience functioning within a professional software team, including proficiency in Python and modern ML frameworks (PyTorch/JAX) at scale.
Benefits
- Competitive base salary, performance-based incentives or bonuses (where applicable), and equity participation.
- Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions.
- Retirement savings with company matching.
- Paid parental leave and inclusive family-building benefits.
- Flexible paid time off, company-wide seasonal breaks, and support for flexible work arrangements that enable sustainable performance.
- Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs.
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