Associate Director, Clinical Data Integration & AI Programming

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Associate Director, Clinical Data Integration & AI Programming based in the United States. This is a hands-on technical leadership role focused on transforming how clinical data is integrated, validated, analyzed, and used across studies and compounds. You will design automated data pipelines, establish standardized clinical data models, and build reusable R and Python dashboards and review tools for study teams. Working across R, Python, SQL, APIs, and enterprise data platforms, you will turn complex clinical data from multiple sources into reliable, analysis-ready information. The role also offers an opportunity to apply generative AI and LLM technologies to data processing, quality review, programming, documentation, and clinical analytics while maintaining appropriate human oversight. You will collaborate closely with Data Management, Clinical Operations, Clinical Development, Safety, Biostatistics, and Statistical Programming teams. As a technical leader, you will also establish engineering standards, mentor programmers, oversee vendors, and guide the evolution of clinical data automation capabilities. This is a remote U.S. opportunity within a regulated pharmaceutical and biotech environment, with occasional domestic and international travel as needed. Accountabilities: • Design, build, and maintain automated clinical data pipelines that ingest information from EDC systems, central laboratories, eCOA and IRT vendors, safety systems, and other external providers, incorporating scheduling, versioning, audit trails, processing logs, and failure alerting. • Own standardized cross-study and cross-compound clinical data models that harmonize source structures, terminology, and variables into consistent, analysis-ready formats, using metadata-driven mapping specifications to accelerate the onboarding of new studies and vendors. • Develop automated data quality and congruency checks covering completeness, structural conformance, visit and date consistency, cross-domain reconciliation, duplicates, outliers, and other discrepancies, routing actionable exceptions to Data Management and study teams. • Automate laboratory, SAE, eCOA, and IRT reconciliation workflows and develop algorithms and data feeds supporting risk-based monitoring and centralized data surveillance. • Build controlled APIs and data-access components that provide consistent and governed access to standardized clinical data for dashboards and downstream programming. • Design, develop, validate, deploy, and maintain interactive R/Shiny and Python dashboards for enrollment, safety, efficacy, laboratory trends, protocol deviations, visit compliance, data cleaning, and study health monitoring. • Build patient profiles, data review listings, edit-check outputs, coding review reports, and medical review applications while transitioning manual or SAS-based processes into reproducible R and Posit solutions. • Develop reusable R and Python packages, modules, visualization components, dashboard templates, metric definitions, and navigation patterns to create consistent and scalable solutions across studies and compounds. • Automate study team communications, including scheduled summaries of enrollment, safety events, data cleaning status, and other critical study metrics. • Partner with Data Management, Clinical Operations, Clinical Development, Safety, Biostatistics, and Statistical Programming to translate clinical review requirements into reliable and intuitive tools while driving adoption through training and documentation. • Establish and operationalize enterprise R/Posit capabilities, including Posit Workbench, Posit Connect, Package Manager, controlled package environments, application publishing, access management, and governed deployment. • Define and enforce development standards covering modular design, reusable libraries, code review, automated testing, error handling, logging, dependency management, Git version control, and CI/CD practices. • Own the lifecycle of pipelines, dashboards, and reusable components from requirements and prototyping through validation, production release, monitoring, enhancement, and retirement, ensuring appropriate documentation, traceability, and change control. • Apply validation, documentation, and quality standards appropriate to regulated clinical environments and serve as a subject matter expert during audits and inspections for solutions under your responsibility. • Evaluate, prototype, and implement AI and generative AI capabilities that can measurably improve clinical data transformation, quality review, dashboard summarization, metadata and standards search, code generation and review, or documentation. • Integrate approved LLM and retrieval-augmented generation capabilities into R and Python applications when they provide clear operational value and can be appropriately governed. • Apply human-in-the-loop review, predefined acceptance criteria, traceability, and validation to AI-assisted outputs, determining when AI methods are appropriate and when deterministic, validated programming is required. • Execute the clinical data integration, dashboard, and automation roadmap across assigned compounds and prioritize work within an agreed backlog. • Provide technical oversight of FSP, CRO, vendor, and consultant resources, including requirements, specifications, deliverable reviews, and acceptance. • Partner with study teams, Data Management, and Biostatistics to gather requirements, resolve issues, and drive adoption of delivered solutions. • Provide training, mentoring, and technical guidance to statistical and clinical programmers on R/Posit, Python, data automation, dashboard development, and responsible AI practices. • Monitor developments in R, Python, Posit, clinical data engineering, and AI applications in drug development and reco