Data Scientist - Trial Surveillance & Analytics (Hybrid)

Trial Surveillance & Analytics (TSA) is a multidisciplinary team of data scientists, statisticians, statistical programmers, and AI specialists in Data & AI Acceleration, part of AbbVie's Data and Statistical Sciences, focused on transforming clinical trial surveillance through AI-enabled data integration, analytics, and automated reporting. TSA delivers integrated medical monitoring and safety surveillance capabilities that enable timely, trusted, and actionable insights across the clinical development lifecycle.
Data Scientists play a key role in designing, developing, and scaling innovative analytics solutions that support clinical trial surveillance, medical monitoring, safety evaluation, and data-driven decision-making. This position combines analytical expertise, business acumen, and modern AI-enabled approaches to transform complex clinical data into meaningful insights that improve operational efficiency and support patient safety. The team is AI-native: agentic coding and AI tools are part of how we program, analyze, and report.
Trial Surveillance & Analytics (TSA) is a multidisciplinary team of data scientists, statisticians, statistical programmers, and AI specialists in Data & AI Acceleration, part of AbbVie's Data and Statistical Sciences, focused on transforming clinical trial surveillance through AI-enabled data integration, analytics, and automated reporting. TSA delivers integrated medical monitoring and safety surveillance capabilities that enable timely, trusted, and actionable insights across the clinical development lifecycle.
Data Scientists play a key role in designing, developing, and scaling innovative analytics solutions that support clinical trial surveillance, medical monitoring, safety evaluation, and data-driven decision-making. This position combines analytical expertise, business acumen, and modern AI-enabled approaches to transform complex clinical data into meaningful insights that improve operational efficiency and support patient safety. The team is AI-native: agentic coding and AI tools are part of how we program, analyze, and report.
Responsibilities:
Partner with cross-functional stakeholders to understand surveillance, analytics, and reporting needs and translate them into scalable, fit-for-purpose solutions.
Develop analytical products, automated workflows, and AI-enabled capabilities that support trial surveillance, medical monitoring, and safety assessment activities, including streamlined solutions from data to analytics to reporting.
Apply statistical methodologies, machine learning techniques, and advanced analytics approaches to generate actionable insights from clinical trial data.
Contribute to the development and maintenance of integrated analytics platforms, data pipelines, and reporting solutions that support ongoing monitoring and safety surveillance.
Transform complex clinical and operational data into clear visualizations, decision-support tools, and meaningful business insights for clinical and safety teams.
Apply AI to transform statistical programming: use agentic coding and generative AI to generate, validate, and document code and outputs, contribute to migrating legacy SAS programs to R, and measure resulting efficiency gains.
Collaborate with team members to establish best practices, reusable analytical assets, and standards that ensure high-quality and consistent user experiences.
Support continuous innovation through experimentation, proof-of-concept development, and evaluation of emerging analytical and AI technologies.
Contribute to operational excellence initiatives by identifying opportunities to improve processes, reduce manual effort, and enhance data quality and reporting efficiency.
Ensure compliance with applicable regulatory requirements, GCP, ICH guidelines, AbbVie policies, and departmental quality standards.
Minimum Qualifications:
Bachelor’s degree statistics, analytics, bioinformatics, data science or equivalent field.
2-5 years of experience in data science, analytics, statistical programming, data engineering, or related disciplines.
Complete or Full / Career proficiency in R, Python or other statistical languages and packages.
Intermediate-level knowledge of statistical and data mining techniques.
Intermediate proficiency with visualization tools such as Spotfire, PyDash/Plotly, or equivalent platforms.
Experience applying machine learning, artificial intelligence, or generative AI technologies to business problems.
Experience developing automated reporting, data products, or end-to-end analytical solutions.
Preferred Qualifications:
Familiarity with clinical data standards (e.g., CDISC SDTM/ADaM) and clinical development processes preferred.
Master’s or PhD degree preferred.
Other Required Skills:
Demonstrated ability to manage multiple priorities and deliver results in a fast-paced environment.
Strong collaboration and communication skills and experience working in cross-functional teams.

Employment type
Full-time

Work arrangement
Hybrid