Senior Manager, Trial Surveillance & Analytics (Hybrid)

AbbVie•San Francisco, CA

3d•$141,500 - $268,500•Hybrid

About The Position

Trial Surveillance & Analytics (TSA) is a newly formed center of excellence in Data & AI Acceleration, part of AbbVie's Data and Statistical Sciences organization. Our mission is to transform how AbbVie conducts ongoing clinical trial surveillance — integrating trial data, analytics and AI so that study teams and patient safety colleagues see emerging safety and efficacy signals early, and can act on them with confidence. This is work upstream, inside ongoing clinical trials, where insight can still change decisions for the study and for patients. The Senior Manager, Trial Surveillance & Analytics is a hands-on, full-stack data scientist who takes a surveillance question from intake to a working solution. You will work in a small team that owns a capability end to end — from the data, through the analysis, to the tool that study teams use — and you will own your solutions from problem statement to a working product that people use. The team is AI-native from day one: agentic coding and AI agents are part of how we work, with room to experiment and to scale what works. You will combine depth in one area (such as statistical programming and reporting, data integration, data visualization, data science and machine learning, or data and software engineering) with enough breadth across clinical data, analytics and AI to deliver on your own, with the support of the team. As an early member of a new center of excellence, you will help define how it works, the standards it sets and the capabilities it builds, partnering across Global Patient Safety, therapeutic area clinical development teams, Statistics, Statistical Programming and data engineering across the portfolio.

Requirements

MS (with 8+ years of experience) or PhD (with 4+ years of experience) in Statistics, Biostatistics, Bioinformatics, Machine learning and AI or a highly related field.
Experience may be in pharmaceutical, biotech, health technology or related industries.
Strong hands-on programming in R and Python, plus SQL and version control (e.g. Git), with a track record of writing production-quality code for end-to-end analytical pipelines.
Demonstrated experience applying generative or agentic AI to analytical or programming work, with measurable results.
Experience working with clinical trial data (e.g. CDISC SDTM/ADaM) and/or real-world data (e.g. EHR, claims, registries).
A track record of building analytical tools or solutions that were adopted and sustained by their users.
Strong communication skills, including the ability to explain analytical and technical concepts in plain language and to influence cross-functional partners without direct authority.

Nice To Haves

Experience designing and building agentic AI systems (e.g. multi-step agents, tool use, retrieval and evaluation), with a track record of solutions delivered and used in production.
Experience in clinical development, drug safety, pharmacovigilance, clinical trial monitoring or regulatory evidence generation.
Knowledge of clinical trial design and of methods for real-world evidence, such as cohort definition, confounding control and external comparators.
Experience building interactive visualizations and data apps with modern frameworks (e.g. Plotly / Dash, Streamlit, Shiny, React / D3) and deploying on cloud platforms (e.g. AWS).
Familiarity with SAS.
Experience leading cross-functional initiatives; formal business or leadership training is a plus.
Publications, presentations or community leadership in data science, statistics or AI.

Responsibilities

Frame surveillance problems with stakeholders before building: understand the decision to be supported, design for adoption, and iterate with users.
Design and build data pipelines and analysis-ready, integrated data on SDTM, ADaM and integrated databases to support safety and efficacy surveillance.
Lead blinded, standardized analytics across studies and compounds to detect emerging safety and efficacy signals early.
Deliver reporting — tables, listings and figures, safety reports and listings — and automate it wherever possible.
Develop applications, dashboards and self-service tools that study teams and patient safety colleagues use themselves; drive their adoption and measure their use.
Apply generative and agentic AI across the analysis workflow, build AI agents on the clinical data foundation, and measure the gain in time and quality.
Integrate clinical trial and real-world data where the science requires it, assessing comparability and data quality first.
Lead projects and small cross-functional pods; coach and mentor team members. May include indirect supervision of employees and contract staff.
Represent the team in cross-functional forums and communicate technical concepts and results clearly to non-technical audiences.
Ensure adherence to applicable regulations, Good Clinical Practice (GCP), ICH guidelines, AbbVie Standard Operating Procedures (SOPs) and functional quality standards.