Hybrid Senior Manager – Analytical AI, Clinical Development - Princeton, NJ bei Bristol Myers Squibb
Bristol Myers Squibb · Princeton, Vereinigte Staaten Von Amerika · Hybrid
- Professional
- Optionales Büro in Princeton
Job Description
ATTENTION MILITARY AFFILIATED JOB SEEKERS - Our organization works with partner companies to source qualified talent for their open roles. The following position is available to Veterans, Transitioning Military, National Guard and Reserve Members, Military Spouses, Wounded Warriors, and their Caregivers. If you have the required skill set, education requirements, and experience, please click the submit button and follow the next steps. Unless specifically stated otherwise, this role is "On-Site" at the location detailed in the job post.
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. 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.
Aligned with Bristol Myers Squibb's mission to advance clinical development through data-driven innovation, the Senior Manager – Analytical AI in Clinical Development will play a key role in designing and deploying machine learning pipelines to optimize clinical trial operations. This individual will engage with cross-functional stakeholders to deliver impactful AI-based solutions and ensure their seamless integration into business processes, driving efficiency and enhancing decision-making across the drug development portfolio.
Key Responsibilities and Major Duties
1. Develop and Deploy Machine Learning Pipelines:
Apply expertise in machine learning to create, implement, and optimize end-to-end pipelines that address critical business problems in clinical study design and execution.
Automate workflows to process and analyze complex datasets, such as clinical trial protocols, real-world evidence, and patient recruitment data.
Ensure the scalability, reliability, and robustness of deployed machine learning solutions for real-time decision support.
2. Drive Stakeholder Engagement and Collaboration:
Partner with cross-functional teams, including IT, medical, development, and biostatistics groups, to co-develop AI-driven solutions aligned with business priorities.
Act as liaison between technical teams and non-technical stakeholders, ensuring alignment on objectives and fostering a collaborative environment for successful solution delivery.
Translate technical insights and model results into actionable strategies for clinical operations teams.
3. Manage AI Product Lifecycle:
Oversee the lifecycle of AI products, from ideation and development to deployment and monitoring.
Develop and execute detailed project plans that align with clinical development timelines, ensuring timely delivery of AI solutions.
Conduct post-deployment analysis to measure the impact of implemented solutions and identify areas for continuous improvement.
4. Stay Ahead of AI Trends in Clinical Development:
Continuously evaluate emerging trends and advancements in data science and AI, integrating cutting-edge techniques such as natural language processing (NLP) and predictive analytics into clinical trial workflows.
Job Category
Minimum Education Required
City*
Additional Qualifications/Responsibilities
Qualifications
Education:
BA/BS in a quantitative field such as data science, computer science, mathematics, or related disciplines.
Advanced degrees (MBA, MS, or Ph.D.) in a relevant field (computer science/computational biology/bioinformatics etc.) preferred.
Experience:
Minimum of 3 years of hands-on experience developing and deploying machine learning pipelines to solve real-world business problems.
Proven ability to deliver measurable impact through AI and advanced analytics solutions.
Technical Skills:
Strong foundation in statistical methods and their application to business problems, preferably in a clinical development setting
Experience designing and implementing supervised, unsupervised and reinforcement learning methods with common machine learning frameworks (TensorFlow, PyTorch, scikit-learn etc.)
Experience with time series forecasting and Monte Carlo simulations
Proficiency in Python and SQL is required
Familiarity with CI/CD workflows and Git/GitHub for version control.
Familiarity with MLOps workflows, cloud-based environments (e.g., AWS, Azure) and workflow orchestration tools (e.g., Airflow, Dagster).
Experience with Natural Language Processing techniques and Large Language Models is highly preferred
Experience working with real-world datasets (RWD) is highly preferred
Experience in bioinformatics/computational biology is a plus
Experience with Streamlit/Dash/Shiny app development is a plus
Soft Skills:
Excellent communication and presentation skills, with a proven ability to translate technical complexity into clear, actionable insights for diverse audiences.
Strong leadership and interpersonal skills to foster stakeholder engagement and cross-functional collaboration.
Preferred Expertise:
Knowledge of drug development and clinical trial operations is highly desirable.
Experience managing AI product lifecycles and integrating solutions into business operations.
The starting compensation for this job is a range from $152,150-$184,400 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.
Final, individual compensation will be decided based on demonstrated experience.
