Associate Director, Clinical Data Integration & AI Programming
$158,000 - $197,900/yr
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 recommend relevant technologies and practices for adoption. Requirements Bachelor’s degree in statistics, biostatistics, computer science, data science, biomedical informatics, or a related scientific discipline; an equivalent combination of education and relevant experience may also be considered. Approximately 10 years of progressively responsible experience in statistical programming, clinical programming, clinical data engineering, or clinical analytics, preferably within a pharmaceutical or biotechnology environment. Demonstrated experience delivering technical solutions spanning multiple clinical studies or programs. Advanced hands-on R programming experience, including Shiny, tidyverse, data.table, R Markdown/Quarto, package development, modular application design, and reproducible workflows. Strong hands-on Python skills for data ingestion, transformation, automation, analytics, and application development. Strong SQL proficiency and experience developing and consuming REST APIs. Demonstrated experience building and deploying interactive clinical dashboards, patient profiles, data review tools, or study monitoring applications used by clinical study teams. Experience designing automated ingestion, transformation, mapping, validation, and refresh processes for structured and semi-structured clinical data. Experience standardizing and integrating EDC, laboratory, eCOA, safety, IRT, and operational data across multiple studies or programs. Experience with Posit/RStudio Workbench, Posit Connect, and Posit Package Manager, or comparable enterprise R and Python environments. Experience with Git, automated testing, CI/CD, logging, monitoring, and controlled software deployment. Strong understanding of clinical trial data and CDISC standards, including SDTM, ADaM, metadata-driven programming, and integrated data structures. Working knowledge of SAS and the ability to bridge established SAS workflows with modern R and Python solutions. Practical understanding of generative AI, LLMs, RAG, and AI-assisted programming in regulated environments, including their limitations and validation requirements. Strong knowledge of GCP, ICH, 21 CFR Part 11, data privacy, access controls, audit trails, and validation expectations for clinical data systems. Excellent written and verbal communication skills, with the ability to explain complex technical concepts clearly to both technical and non-technical stakeholders. Strong organizational and prioritization skills, with the ability to balance competing initiatives and deadlines while maintaining high-quality execution. Willingness and ability to travel domestically and internationally as needed. Ability to work independently in a remote environment while collaborating effectively across multidisciplinary teams. Ability to regularly sit, stand, walk, use hands, and perform other standard office and remote-work activities, with occasional lifting or moving of items up to 20 pounds. Benefits Annual base salary range of $158,000–$197,900 USD . Eligibility for discretionary bonus and equity awards based on factors including individual and organizational performance. Competitive base, bonus, new-hire, and ongoing equity opportunities. Medical, dental, and vision insurance. Employer-paid life, disability, business travel, and Employee Assistance Program coverage. 401(k) plan with a fully vested 1:1 company match on contributions up to 5%. Employee Stock Purchase Plan with a two-year purchase price lock-in. 15+ vacation days. 13–15 paid holidays, including an office closure between December 24 and January 1. 10 days of paid sick time. Paid parental leave. Tuition assistance. Remote work from within the United States. Opportunity to work at the intersection of clinical data engineering, statistical programming, automation, dashboards, and responsible AI within a regulated environment. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. 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