Associate Director, AI and Machine Learning

will partner with stakeholders across global statistics and data science, clinical development and operation, research, transitional medicine, regulatory, safety, medical writing, digital and technology teams to identify, design and implement AI and machine learning solutions that improve research productivity, development efficiency, and decision making.

Essential Functions of the Job:

• Lead the strategy, design, and implementation of AI and machine learning solutions to support R&D digital transformation and operational efficiency.
• Drive the adoption of AI across R&D by identifying high-impact use cases, building business cases, and partnering with scientific, statistical, clinical, and technology stakeholders.
• Develop and deploy AI/ML models, tools, and platforms that improve the efficiency of digitalization systems, including clinical trial design, clinical data workflows, statistical analysis processes, document generation, knowledge management, and decision-support systems.
• Evaluate emerging AI technologies, including generative AI, large language models, intelligent agents, automation frameworks, and advanced machine learning approaches, and assess their applicability in regulated pharmaceutical R&D environments.

Supervisory Responsibilities:

• This is an individual contributor role with significant cross-functional leadership responsibilities. The role does not have direct people management responsibilities at present but may evolve to include supervisory or people-leadership responsibilities as the AI function and portfolio expand.

Computer Skills:

The candidate should have strong hands-on experience with modern data science, AI, machine learning models, tools and platforms, including

• Proficiency in programming languages commonly used for data science and AI, such as Python and R.
• Experience with machine learning and deep learning frameworks such as scikit-learn, PyTorch, TensorFlow, or equivalent tools.
• Working knowledge of generative AI, large language models, prompt engineering, retrieval-augmented generation, AI agents, and related application-development frameworks.
• Experience with AI-native programming practices, including the use of AI coding assistants, agentic development tools, prompt-based code generation, automated code review, test generation, documentation generation, and AI-assisted debugging.
• Experience with data manipulation, statistical analysis, visualization, and reporting tools such as SQL, pandas, NumPy, SAS, R Shiny, Power BI, or equivalent platforms.
• Familiarity with cloud-based data and AI platforms, such as AWS, Azure, Databricks.
• Understanding of MLOps, including model development, version control, validation, deployment, monitoring, and lifecycle management.
• Familiarity with data privacy, cybersecurity, access control, audit trail, and documentation

• in regulated pharmaceutical or healthcare environments.

Other Qualifications:

• Significant experience applying AI, machine learning advanced analytics, or data science in pharmaceutical, biotechnology, healthcare, clinical development, or another regulated environment.
• Demonstrated ability to lead cross-functional initiatives and influence stakeholders across scientific, technical, operational, and leadership teams.
• Knowledge of regulatory expectations, data privacy, model validation, responsible AI, and governance considerations in pharmaceutical R&D.
• Strong problem-solving skills with the ability to translate complex business challenges into practical, scalable AI solutions.
• Excellent communication, presentation, and stakeholder management skills.

Education Required:

• Advanced degrees in statistics, biostatistics, computer science, data science, machine learning, bioinformatics, engineering, mathematics, or related quantitative field. PhD or master’s degree preferred.

Travel:

• Willingness to travel domestically within U.S. and occasionally international as required to support portfolio governance, stakeholder alignment and project execution.