Senior Principal Scientist - Machine Learning Protein Design

Key Skills

Aartificial intelligence, Analytical and Problem solving, Bioinformatics, Continuous Integration & Continuous Delivery - CI/CD, Deep Learning, Effective communication skills, GANs, Machine learning techniques, Python Programming, PyTorch, R Programming, TensorFlow

Job Description

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. Read more: careers.bms.com/working-with-us.

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. We do this in close partnership with scientific and clinical experts in the field, both inside and outside the company.  We perform innovative science to empower key data-driven decisions across a rich pipeline of next-generation medicines. In doing so, our work transforms the lives of patients, as well as our own lives and careers.

Here, you’ll get the chance to grow and thrive through opportunities that are uncommon in scale and scope. You’ll pursue innovative ideas while advancing professionally alongside some of the brightest minds in biopharma.

JOB DESCRIPTION 

We are seeking an experienced, creative, and collaborative scientist with expertise in machine-learning based protein design. You will be responsible for the research and development of innovative computational approaches and workflows to design and optimize drug-like properties that can help us develop novel biologics. This role provides the opportunity to leverage large-scale proprietary datasets and work with subject matter experts on cross-functional teams to solve challenging problems in how we discover and engineer the next generation of biotherapeutics. 

We are deeply passionate about what we do here at Bristol Myers Squibb because we know that our work helps deliver truly innovative and life-changing therapies for complex diseases of unmet medical need. If this inspires you as well, we’re excited to have you join us! 

RESPONSIBILITIES 

• Pioneer machine learning research at the cutting edge of research and development to create active learning systems for biologics engineering  
• Partner with project teams to advance predictive approaches to guide lab experiments, collaborate on generation of high-throughput data for model training, and update predictive models based on a design-make-test-learn cycle 
• Develop and implement computational approaches and prediction algorithms for drug-like attributes that drive molecular optimization and selection 
• Leverage state-of-the-art research in structure and sequence-based machine learning approaches on real world multi-modal datasets 
• Communicate findings and recommend follow-up actions in multiple settings such as 1:1, seminars, group, and project meetings. 

BASIC QUALIFICATIONS 

• Bachelor's Degree in computer science, engineering, statistics, or similar and 10+ years of academic / industry experience
• or Master's Degree in computer science, engineering, statistics, or similar and 8+ years of academic / industry experience
• or PhD in computer science, engineering, statistics, or similar and 6+ years of academic / industry experience

PREFERRED QUALIFICATIONS

• Ph.D. in computer science, engineering, statistics, or similar with 8+ years of relevant experience. 
• Deep expertise in modern neural network architectures including diffusion models, transformers, autoencoders, GNNs, and attention models. Familiarity with PyTorch/Tensorflow/JAX and its ecosystem, as well as other platforms such as scikit-learn, and design of effective experiments and metrics. 
• In-depth understanding of deep learning, Monte-Carlo tree search, Bayesian optimization, genetic, multi-task, and reinforcement learning algorithms. 
• Proven track record of contributing to and helping advance multi-disciplinary team projects.
• Expertise with a high-level programming language such as R or Python for sophisticated data analysis and reproducible research practices. 
• Scientific curiosity with an ability to identify questions machine learning approaches can address, and the skills to develop solutions both independently and collaboratively. 

• Excellent problem-solving skills and teamwork. 
• Strong communication, data presentation, and visualization skills. 
• Knowledge in biochemistry or protein engineering is a plus. 

• Experience with modern software engineering tools and practices (git, CI/CD, containerization) is a plus. 
• Understanding of high-performance and/or cloud computing is desirable. 

The starting compensation for this job is a range from $159,000 to $185,000 plus incentive cash and stock opportunities (based on eligibility).

The starting pay rate takes into account characteristics of the job, such as required skills and where the job is performed.