Director, Safety Data Science

Responsibilities

• Develop, validate, deploy, and optimize statistical, machine learning, deep learning, natural language processing, and generative AI solutions supporting medical safety and business objectives.
• Apply advanced analytical methods to quality monitoring, operational analytics, risk monitoring, translational analytics, and strategic decision-making.
• Evaluate and implement emerging technologies and methodologies that improve organizational capabilities.
• Define requirements for scalable data pipelines, analytical data assets, and reusable analytics products.
• Work with healthcare, safety, real-world, and other enterprise data sources to generate actionable insights.
• Lead development of analytical capabilities that improve efficiency, decision-making, and patient safety outcomes.
• Partner with Information Technology and Digital organizations on collaborative initiatives.
• Develop dashboards, visualizations, and analytical applications that communicate insights and support business decisions.
• Provide technical expertise and analytical leadership to cross-functional stakeholders.
• Communicate insights, recommendations, and strategic opportunities to leadership teams.
• Contribute to scientific publications, industry collaborations, conferences, and external engagements.
• Lead strategic initiatives with increasing organizational complexity and impact.
• Mentor and develop analytics professionals while fostering a culture of innovation, collaboration, and continuous learning.
• Drive organizational adoption of new analytical capabilities and best practices.

Qualifications

• Advanced degree (PhD preferred; Master's degree with significant relevant experience considered) in Statistics, Biostatistics, Data Science, Computer Science, Mathematics, Bioinformatics, Biomedical Informatics, Epidemiology, or a related quantitative discipline.
• Significant experience leading advanced analytics, machine learning, artificial intelligence, data science, healthcare analytics, medical safety analytics, or related scientific functions.
• Experience supporting medical safety, pharmacovigilance, real-world evidence, healthcare analytics, or related life-science functions is required.
• Demonstrated ability to lead analytics, AI/ML, or quantitative scientific initiatives.
• Demonstrated ability to build end-to-end analytical solutions from data acquisition through deployment and business adoption.
• Demonstrated ability to translate analytical concepts into scalable, production-grade solutions delivering measurable business value.
• Demonstrated ability to lead projects, teams, and cross-functional initiatives.
• Experience working with healthcare, safety, real-world, or enterprise-scale data environments is preferred.
• Experience presenting scientific work to technical and non-technical audiences is required.
• Proficiency in Python, R, and SQL.
• Expertise in statistical methods, predictive modeling, machine learning, deep learning, generative AI, and large language models.
• Understanding of cloud-based analytical architectures.
• Experience deploying AI/ML solutions using modern MLOps, CI/CD, and model lifecycle management practices.
• Understanding of data engineering, feature engineering, and scalable analytical architectures.
• Understanding of regulatory expectations, validation principles, governance requirements, and responsible AI practices.
• Strong analytical, problem-solving, communication, and stakeholder management skills.
• Ability to bridge business strategy, scientific methodology, and technical implementation to deliver organizational impact.