ABOUT DEEP ORIGIN

Deep Origin is a biotechnology company accelerating drug discovery through AI-powered tools. Our platforms simplify R&D, simulate biology, and empower scientists to solve diseases and extend human healthspan through software solutions and partnerships. We integrate advanced computational methods with experimental data to model biological systems at scale.

REQUIREMENTS

• PhD (0-2 years) or MS (2-5 years) of relevant experience in Systems Biology, Computational Pharmacology, Applied Mathematics, or related field;
• Strong background in ODE/PDE modeling, numerical simulation, and parameter estimation;
• Experience with pharmacokinetic model development;
• Proficiency in Python;
• Basic understanding of drug metabolism and pharmacokinetics (DMPK) concepts;
• Strong statistical analysis skills and experience with Bayesian or frequentist inference methods;
• Ability to critically analyze data and translate findings into actionable predictions and computational models;
• Collaborative mindset, comfortable working in both autonomous and team-based settings;Adaptability to thrive in a fast-paced, deadline-driven environment.

ROLE DESCRIPTION:

We are seeking a skilled Data Scientist with expertise in quantitative pharmacokinetics, parameter estimation, and metabolic modeling. You need to develop hybrid computational frameworks that combine ordinary differential equation-based (ODE-based) simulations with machine learning methods to predict metabolic parameters, distribution profiles, and biotransformation outcomes.
This is a highly interdisciplinary role requiring strong mathematical modeling skills, statistical acumen, and experience integrating diverse datasets into robust predictive models. Combining expertise in systems pharmacology, machine learning, and data-driven modeling, you will develop hybrid approaches that unite mechanistic simulations with AI-driven predictions, powering Deep Origin’s next-generation AI-powered metabolism and pharmacokinetic simulation platform.

KEY RESPONSIBILITIES:

• Develop and calibrate ODE-based models for drug metabolism and distribution;
• Predict key metabolic and pharmacokinetic parameters (e.g., Vmax, Km);
• Model Phase I and Phase II metabolic networks at the systems level;
• Simulate compound distribution inside a defined compartment;
• Apply statistical inference and optimization techniques to fit models to experimental and clinical data;
• Integrate genetic polymorphism data to simulate inter-individual variability in metabolism;
• Engage with colleagues across disciplines to iteratively develop and strengthen models.