Sr. Data Scientist – Computational Drug Discovery

Amgen•Burnaby, BC

7d•CA$117,808 - CA$159,388

About The Position

Join Amgen’s Mission of Serving Patients At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do. Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. Amgen is advancing a broad and deep pipeline of medicines to treat cancer, heart disease, inflammatory conditions, rare diseases, and obesity and obesity-related conditions. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives. Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career. Sr. Data Scientist – Computational Drug Discovery What you will do Let’s do this. Let’s change the world. We are seeking a Data Scientist with strong expertise in molecular simulation and machine learning to join the AI & Data for Engineered Biologics team within Amgen’s Large Molecule Discovery (LMD) organization. In this vital role you will advance Amgen’s discovery programs through the development and application of molecular simulation, structural modeling, and machine learning approaches for large-molecule therapeutic design. You will work at the interface of physics-based modeling and data-driven prediction to generate mechanistic insight, guide experimental design, and enable candidate optimization across biologics discovery workflows. The successful candidate will work in a highly collaborative, multidisciplinary environment, partnering with structural biology, protein engineering, computational and data sciences, and experimental discovery teams to impact programs from target and hit validation through lead optimization and candidate selection.

Requirements

PhD in Computational Biology, Computational Chemistry, Theoretical Chemistry, Biophysics, or a related field
Or Master’s degree and 3 years of directly related experience
Or Bachelor’s degree and 5 years of directly related experience

Nice To Haves

Postdoctoral or equivalent research experience in molecular simulation, computational biophysics, or AI-accelerated drug discovery
Deep expertise in molecular dynamics simulations of biomolecular systems, including enhanced sampling, rare-event methods, free-energy or kinetics analysis, and interpretation of conformational ensembles
Strong programming skills in Python and experience with modern ML frameworks (e.g., PyTorch) and molecular simulation / modeling software such as GROMACS, OpenMM, Amber, NAMD, or related tools
Experience deriving machine learning features or representations from molecular simulation trajectories and integrating them with sequence, structural, and experimental data
Proven ability to apply machine learning methods such as supervised learning, deep learning, generative modeling, active learning, or optimization methods to molecular design or drug discovery problems
Experience working with large-scale datasets and high-performance computing environments, including cloud-based platforms (e.g., AWS)
Experience integrating molecular modeling and machine learning with structural and biophysical data, including cryo-EM, X-ray, HDX, or related experimental readouts
Familiarity with software engineering best practices for reproducible scientific computing, including version control (git), testing, containerization (Docker), and workflow orchestration tools
Experience supporting biologics, protein engineering, or large-molecule therapeutic discovery is strongly preferred
Strong scientific communication skills, with a demonstrated publication record
Demonstrated ability to influence cross-functional teams by translating computational results into clear, actionable scientific recommendations

Responsibilities

Design and execute molecular dynamics simulations, including enhanced-sampling and kinetics-focused approaches, to study protein structure, conformational dynamics, binding mechanisms, and biomolecular interactions relevant to large-molecule therapeutic discovery
Develop and apply machine learning models leveraging molecular simulation, sequence, structural, repertoire, and experimental data to predict molecular properties, prioritize designs, and generate testable hypotheses
Build integrated modeling frameworks that combine multi-modal biological data to enable therapeutic design and data-driven decision-making
Incorporate physics-based insights from molecular simulations (e.g., conformational ensembles, dynamics) into machine learning models to improve interpretability and predictive accuracy
Develop and scale robust computational workflows, reusable pipelines, and modeling platforms to ensure reproducibility and efficiency across discovery programs
Mentor and provide technical guidance to junior data scientists and computational scientists, supporting best practices in modeling and analysis
Collaborate cross-functionally with structural biology, protein engineering, and experimental teams to define computational strategies, prioritize experiments, and translate insights into actionable outcomes
Evaluate and implement emerging methods in machine learning, generative modeling, and molecular simulation to enhance the Amgen’s computational discovery toolkit
Communicate scientific findings and recommendations clearly through presentations, technical documentation, and publications