We are seeking a data scientist to help lead the development of our digital solution. This individual should have domain expertise in computational science, including but not limited to computational biology, computational chemistry, computational physics/mechanics, materials modeling and simulation. As one of the earliest scientific hires, this individual will design computational frameworks that integrate numerical simulation, materials modeling, data analysis, and machine learning.

This role includes significant scientific ownership, the opportunity to serve as the Principal Investigator (PI) on a federal commercialization grant (NSF), and direct influence over the company’s technical direction, modeling strategy, and research roadmap.

Required Qualifications

• Ph.D. in Biomedical or Chemical Engineering, Materials Science, Biology, Chemistry, or other relevant areas
• Domain expertise in computational science, including but not limited to:
  • Computational Biology & Chemistry
  • Computational Physics & Mechanics
  • Materials Modeling & Simulation
• Experience with software development in C/C++, Python, or other programming languages
• Strong background in numerical analysis and optimization
• Strong communication skills
• Self-motivated individual who is enthusiastic about defining problems and exploring new ideas in a startup setting

Optional Qualifications

• Experience with data science and machine learning tools and frameworks (e.g., AWS/other cloud services, PyTorch, TensorFlow, etc.)
• Industry experience

Work Context

• This role is primarily computer-based and involves developing computational models, running simulations, and collaborating with a multidisciplinary team of engineers, scientists, and clinical advisors. The environment is fast-paced and research-driven, with high autonomy and regular communication of scientific findings to both technical and non-technical stakeholders.
• Work takes place in a hybrid office/lab setting within the CvilleBioHub BioAccelerator, with occasional interactions in lab or device development areas following standard safety practices. Decisions made in this role have significant impact on research direction, material design, and grant milestones, and the work includes a mix of routine computational tasks and complex, open-ended scientific challenges typical of an early-stage startup.