Senior Manager, Analytical AI Clinical Development

Bristol Myers Squibb4 Locationsonsite$167k–$203kView the original posting at Bristol Myers Squibb ↗

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it. Job Position Summary 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. 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 La

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