Associate Director, Clinical Data Integration & AI Programming

About Acadia Pharmaceuticals Acadia is committed to turning scientific promise into meaningful innovation that makes the difference for underserved neurological and rare disease communities around the world. Our commercial portfolio includes the first and only FDA-approved treatments for Parkinson’s disease psychosis and Rett syndrome. We are developing the next wave of therapeutic advancements with a robust and diverse pipeline that includes mid- to late-stage programs in Alzheimer’s disease psychosis and Lewy body dementia psychosis, along with earlier-stage programs that address other underserved patient needs. At Acadia, we’re here to be their difference. Position Summary The Associate Director is a hands-on technical role responsible for automating how clinical data are integrated across assigned compounds and studies, and for building the reusable R and Python dashboards and data review tools that make those data usable by study teams. Working in R, Python, SQL, and APIs on the Posit platform, the role builds automated ingestion pipelines with incoming quality and congruency checks, maps disparate source structures into a standardized analysis-ready data model and delivers validated dashboards and review tools built on that foundation. It additionally evaluates and implements applied AI and generative AI capabilities where they measurably improve data processing, quality review, or programming productivity, always under human oversight and in accordance with regulations, SOPs, and data privacy requirements. Primary Responsibilities Clinical Data Integration and Automation

• Designs, builds, and maintains automated pipelines that ingest clinical data from EDC systems, central laboratories, eCOA and IRT vendors, safety systems, and other external providers at defined refresh frequencies, with scheduling, versioning, audit trails, processing logs, and failure alerting
• Owns the standardized cross-study and cross-compound clinical data model that harmonizes source structures, terminology, and variables into consistent, analysis-ready form, and establishes metadata-driven mapping specifications so new studies and vendors onboard without rebuilding pipelines
• Develops automated incoming data quality and congruency checks, including completeness, structural conformance, visit and date consistency, cross-domain reconciliation, duplicates, and outliers, and routes actionable exception outputs to Data Management and study teams
• Automates laboratory, SAE, eCOA, and IRT reconciliation workflows and develops congruency flags that identify discrepancies across studies, compounds, and source systems
• Develops algorithms and data feeds supporting risk-based monitoring and centralized data surveillance
• Builds controlled APIs and data access components that provide consistent, governed access to standardized clinical data for dashboards and downstream programming R and Python Dashboards and Data Review Tools
• Designs, develops, validates, deploys, and maintains interactive R/Shiny and Python dashboards for clinical data review and study health monitoring, covering enrollment, adverse and serious adverse events, efficacy data review, laboratory trends, protocol deviations, visit compliance, data cleaning status, and operational metrics
• Builds interactive patient profiles, data review listings, edit check outputs, coding review reports, and medical review applications, transitioning these deliverables from manual or SAS-based workflows into reproducible R/Posit solutions
• Develops reusable R and Python packages, modules, visualization components, and dashboard templates, together with common metric definitions and navigation patterns, so outputs are consistent and comparable across studies and compounds
• Develops automated study team communications, including scheduled summaries of enrollment, safety events, data cleaning status, and other key study metrics
• Partners with Data Management, Clinical Operations, Clinical Development, Safety, Biostatistics, and Statistical Programming to translate clinical review needs into reliable, intuitive dashboard functionality and drives adoption through training and documentation Engineering Standards, Validation, and Platform
• Operationalizes R/Posit capabilities within Biometrics, including Posit Workbench, Posit Connect, Package Manager, controlled package environments, application publishing, access management, and governed deployment
• Establishes R and Python development standards covering modular design, reusable code libraries, code review, automated testing, error handling, logging, and dependency management, and applies Git version control and CI/CD practices to pipelines and applications
• Owns the lifecycle of pipelines, dashboards, and reusable components from requirements and prototyping through validation, production release, monitoring, enhancement, and retirement, with documentation, traceability, and change control appropriate to a regulated environment
• Applies established validation, documentation, and quality standards to assigned pipelines and applications, and supports audits and inspections as subject matter expert for the solutions the incumbent has built Applied AI Capabilities
• Evaluates, prototypes, and implements AI and generative AI capabilities that measurably improve clinical data transformation, quality review, dashboard summarization, metadata and standards search, code generation and review assistance, or documentation drafting
• Integrates approved LLM and retrieval augmented generation capabilities into R and Python applications where they deliver clear operational benefit and can be appropriately governed, including natural language querying of approved internal standards, specifications, and study documentation
• Applies human in the loop review, predefined acceptance criteria, traceability, and validation to AI-assisted outputs, and determines when AI-assisted methods are appropriate versus when deterministic, validated programming is required Operational Leadership and Collaboration
• Executes the clinical data integration, dashboard, and automation roadmap across assigned compounds, and prioritizes work within an agreed backlog
• Leads technical and process improvement initiatives within assigned compounds and workstreams, and recommends sourcing approaches and tooling for consideration by functional leadership
• Provides technical oversight of FSP, CRO, vendor, and consultant resources supporting assigned data integration and dashboard development work, including specification, review, and acceptance of their deliverables
• Partners with study teams, Data Management, and Biostatistics at the study and compound level to gather requirements, resolve issues, and drive adoption of delivered tools
• Provides training, mentoring, and technical guidance to statistical and clinical programmers on R/Posit, Python, data automation, dashboard development, and responsible AI practices
• Monitors developments in R, Python, Posit, clinical data engineering, and AI use in drug development, and recommends relevant advances for adoption
• Other responsibilities as assigned. Education/Experience/Skills
• Bachelor’s degree in statistics, biostatistics, computer science, data science, biomedical informatics or a related science field. An equivalent combination of relevant education and experience may be considered
• Targeting 10 years of progressively responsible experience in statistical programming, clinical programming, clinical data engineering, or clinical analytics, preferably in a pharmaceutical or biotech environment. Demonstrated experience delivering technical solutions that span multiple studies is required
• Advanced hands-on R programming, including Shiny, tidyverse, data.table, R Markdown/Quarto, package development, modular application design, and reproducible workflows
• Strong hands-on Python for data ingestion, transformation, aut