Working with Us
Challenging. Meaningful. Life-changing. Those aren't words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You'll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams rich in diversity. Take your career farther than you thought possible.
Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives.
When you join BMS, you are joining a diverse, high-achieving team united by a common mission.
The Informatics and Predictive Sciences (IPS) mission is to Pioneer, Partner and Predict to drive transformative insights for patient benefit. IPS conducts applied computational research in areas that include genomic, structural and molecular informatics, computational and systems biology, patient selection and translational biomarker research, and broader fields including knowledge science, epidemiology and machine learning—across the full lifecycle of drug discovery and development and across all therapeutic areas at BMS.
The ideal candidate will work in a computational research group within a global IPS organization and alongside Cancer Immunology & Cell Therapy Thematic Research Center (CICT TRC) colleagues to optimize engineering of cell therapies, identify molecular mechanisms to improve efficacy, and reduce potential toxicities.
Responsibilities
Working in collaboration with computational, biological and clinical scientists across the Bristol Myers Squibb IPS, and CICT organizations, responsibilities include but are not limited to:
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 teams 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.
Basic Qualifications:
Bachelor's degree with 8+ years of academic/industry experience, or Master's degree with 6+ years of academic/industry experience, or PhD with 4+ years of academic/industry experience.
Preferred Qualifications:
PhD degree with 4+ years experience in computer science, computational biology, bioinformatics, biomedical informatics, genetics, statistics or related fields with a strong publication record.
Extensive experience developing, implementing, and training novel and scalable machine learning architectures on large multi-modal datasets in either scientific research or product development settings.
Extensive experience with deep learning methodologies including transformers, GNNs, and CNNs as well as semi-supervised and self-supervised learning strategies.
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 in a fast-paced environment.
Familiarity with cell biology, immunology, oncology, or biologics a plus.
Fluent verbal and written English language skills.
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