Develop hybrid modeling approaches that integrate mechanistic models with machine learning to optimize cell culture media, improve media design success rates, and accelerate development timelines.
Build intuitive, user-friendly interfaces for implementing the hybrid models and deploy web applications on AWS EC2.
Collaborate with cross-functional teams to understand scientific and operational requirements and develop modeling solutions that optimize various aspects of the bioproduction workflow.
Communicate results and insights effectively to interdisciplinary project teams and stakeholders.
Provide training and mentorship to R&D scientists and junior data scientists, supporting skill development and adoption of modeling tools.
Contribute to grant proposals and other funding initiatives to support new data science and modeling capabilities.

Requirements

Ph.D. in Computational Biology, Bioinformatics, Mathematics, Data Science, or related field.
M.S. with 3+ years of industry or academic experience in mechanistic modeling, machine learning, or bioproduction applications.
Experience in stoichiometric modelling of cell metabolism including metabolic flux analysis (MFA), flux balance analysis (FBA) etc.
Demonstrated experience in mechanistic modeling, including development and application of first-principles models such as ODE/PDE-based, kinetic, mass-balance, or systems biology models.
Proficiency in machine learning methods such as regression, classification, neural networks, ensemble methods, or Gaussian processes.
Hands-on experience in building hybrid (mechanistic + ML) models and applying them to complex, data-driven problems such as bioprocess optimization, systems biology, digital twins, and process control.
Experience working with high-dimensional and time-series experimental data.
Strong programming skills in Python, with proficiency in relevant ML and modeling libraries such as scikit-learn, TensorFlow, and PyTorch.
Proven experience in building and deploying interactive dashboards in Python, ideally using Dash or similar frameworks.
Experience working with cloud-based data platforms (e.g. Databricks) and proficiency with version control systems such as GitHub.

Benefits

A choice of national medical and dental plans, and a national vision plan, including health incentive programs
Employee assistance and family support programs, including commuter benefits and tuition reimbursement
At least 120 hours paid time off (PTO), 10 paid holidays annually, paid parental leave (3 weeks for bonding and 8 weeks for caregiver leave), accident and life insurance, and short- and long-term disability in accordance with company policy
Retirement and savings programs, such as our competitive 401(k) U.S. retirement savings plan
Employees’ Stock Purchase Plan (ESPP) offers eligible colleagues the opportunity to purchase company stock at a discount

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

mechanistic modelingmachine learningstoichiometric modelingmetabolic flux analysisflux balance analysisfirst-principles modelsordinary differential equationspartial differential equationsPythondata-driven problem solving

Soft Skills

communicationcollaborationmentorshiptraininginterdisciplinary teamwork

Certifications

Ph.D. in Computational BiologyM.S. in Data Science