Remote Computational Pharmacokinetics & Metabolism Data Scientist

Description:

• Deep Origin is a biotechnology company focused on accelerating drug discovery through AI-powered tools.
• The company integrates advanced computational methods with experimental data to model biological systems at scale.
• The role is for a Data Scientist with expertise in quantitative pharmacokinetics, parameter estimation, and metabolic modeling.
• The candidate will develop hybrid computational frameworks that combine ordinary differential equation-based (ODE-based) simulations with machine learning methods.
• Responsibilities include predicting metabolic parameters, distribution profiles, and biotransformation outcomes.
• This position requires strong mathematical modeling skills, statistical acumen, and experience in integrating diverse datasets into robust predictive models.
• The role combines expertise in systems pharmacology, machine learning, and data-driven modeling to develop hybrid approaches for Deep Origin’s AI-powered metabolism and pharmacokinetic simulation platform.

Requirements:

• A PhD (0-2 years) or MS (2-5 years) in Systems Biology, Computational Pharmacology, Applied Mathematics, or a related field is required.
• A strong background in ODE/PDE modeling, numerical simulation, and parameter estimation is necessary.
• Experience with pharmacokinetic model development is essential.
• Proficiency in Python is required.
• A basic understanding of drug metabolism and pharmacokinetics (DMPK) concepts is needed.
• Strong statistical analysis skills and experience with Bayesian or frequentist inference methods are required.
• The ability to critically analyze data and translate findings into actionable predictions and computational models is necessary.
• A collaborative mindset and comfort in both autonomous and team-based settings are important.
• Adaptability to thrive in a fast-paced, deadline-driven environment is required.

Benefits:

• The opportunity to work on impactful problems at the intersection of AI, chemistry, and biology.
• Collaboration with multidisciplinary teams of scientists is encouraged.
• The chance to shape next-generation tools for predictive drug discovery is offered.