Principal Scientist, Machine Learning

$129,000 - $177,100/Yr

Bristol-Myers Squibb - Seattle, WA

Full-time - Principal

Seattle, WA

Chemical Manufacturing

About the position

Working at Bristol Myers Squibb as a Principal Scientist in Machine Learning is an opportunity to engage in challenging and meaningful work that has the potential to change lives. The role is situated within the Informatics and Predictive Sciences (IPS) team, which is dedicated to pioneering innovative solutions that drive transformative insights for patient benefit. This position involves conducting applied computational research across various domains, including genomic, structural, and molecular informatics, as well as computational and systems biology. The IPS team collaborates closely with the Cancer Immunology & Cell Therapy Thematic Research Center (CICT TRC) to enhance the engineering of cell therapies, identify molecular mechanisms that improve efficacy, and mitigate potential toxicities. As a Principal Scientist, you will be responsible for developing large multimodal foundation models that integrate both structured and unstructured data, specifically for applications in cell engineering and target discovery. You will also create deep learning methodologies to analyze single-cell and spatial transcriptomics datasets, extracting critical features that can inform cancer immunology drug discovery. Staying abreast of the latest advancements in machine learning methodologies and their applications in drug discovery is essential. Collaboration with cross-functional teams will be a key aspect of your role, as you will design perturbation experiments and recommend novel drug targets based on your findings. In addition to your research responsibilities, you will participate in the authorship of scientific reports and present your methods and conclusions to a publishable standard. This role offers a unique opportunity to work within a diverse, high-achieving team that is united by a common mission to improve patient outcomes through innovative research and development.

Responsibilities

• Develop large multimodal foundation models (LLMs/LMMs) integrating structured and unstructured data for cell engineering and target discovery applications
• Develop deep learning-based methodologies for characterizing single cell and spatial transcriptomics datasets to infer features critical for cancer immunology drug discovery
• Stay up to date on state-of-the-art machine learning methodologies and their applications in cell engineering and drug discovery
• Collaborate with cross-functional team to design perturbation experiments and recommend novel drug targets
• Participate in authorship of scientific reports in static and interactive formats, and present methods and conclusions to publishable standards

Requirements

• Bachelor's degree with 8+ years of academic/industry experience
• Master's degree with 6+ years of academic/industry experience
• PhD with 4+ years of academic/industry experience
• Extensive experience developing, implementing, and training novel and scalable machine learning architectures on large multi-modal datasets
• Extensive experience with deep learning methodologies including transformers, GNNs, and CNNs
• Expertise in Python, PyTorch, and open-source data processing and visualization packages
• Demonstrated experience working with cloud computing services (e.g. AWS) and job schedulers (e.g. Slurm)
• Proven problem-solving skills, collaborative nature and flexibility across multiple research domains
• Ability to work independently and as a member of a global analytical research team
• Fluent verbal and written English language skills

Nice-to-haves

• PhD degree with 4+ years experience in computer science, computational biology, bioinformatics, biomedical informatics, genetics, statistics or related fields with a strong publication record
• Familiarity with cell biology, immunology, oncology, or biologics a plus

Benefits

• Competitive salary
• Incentive cash and stock opportunities
• Wide variety of competitive benefits, services and programs to support personal and professional goals