Associate Director, Clinical Solutions Engineering

The Opportunity:

• Clinical Solutions Leadership: Lead the development and implementation of in-house applications, analytics, and AI-enabled solutions that support other clinical functions. Combine hands-on technical leadership with team management and delivery oversight to build reliable, scalable capabilities.

• Team Management and Development: Manage and mentor engineers, programmers, and technical contractors. Establish clear goals, provide regular feedback and coaching, support career development, and build a collaborative team culture grounded in technical excellence, accountability, and continuous learning.

• Solution Strategy and Delivery: Partner with functional leaders to define clinical solutions roadmap, prioritize business needs, and translate requirements into achievable implementation plans. Oversee project scope, resources, timelines, dependencies, and delivery risks while balancing new development and ongoing support.

• Dashboard and Application Development: Lead and contribute to the development of applications, dashboards, reports, backend dataflows, and data transformations that support clinical trial data review, analysis, performance metrics, and operational insights.

• AI Solution Architecture: Guide the design and delivery of scalable AI solutions and governed automation in regulated environments. Establish appropriate approaches for model selection, retrieval, prompt engineering, tool integration, evaluation, guardrails, and human review.

• AI Agent Development: Oversee and contribute to the development of Retrieval-Augmented Generation (RAG) agents and LLM-enabled applications supporting multi-step workflows, including study document review, specification interpretation, script generation, data mapping, reporting, output verification, and documentation.

• Programming and Workflow Modernization: Lead approaches for translating, modernizing, and validating analytical code across programming languages, with emphasis on SAS-to-R conversion and modernization of legacy workflows. Establish methods to demonstrate analytical equivalence, reproducibility, and traceability.

• Clinical Data and System Integration: Collaborate with Clinical Systems, IT, and business stakeholders to integrate solutions into clinical data platforms, statistical computing environments, and operational systems. Guide technical design decisions and support effective implementation and adoption.

• Technical Leadership and Knowledge Sharing: Remain actively involved in architecture, prototyping, code review, and resolution of complex technical issues. Develop reusable frameworks, promote sound engineering practices, and provide training and mentoring that strengthen team capabilities.

Required Skills, Experience and Education:

• Bachelor’s degree in Computer Science, Engineering, Data Science, Statistics, Bioinformatics, Health Informatics, or a related technical field; advanced degree preferred.

• 9+ years of progressive experience in software engineering, data engineering, statistical programming, clinical systems, AI/ML engineering, solutions engineering, or related technical roles, including technical leadership and accountability for solution delivery. An advanced degree may be considered in lieu of two years of relevant experience.

• Demonstrated experience leading technical teams or workstreams, including setting priorities, assigning work, reviewing deliverables, and coaching engineers or programmers. Experience managing direct reports and supporting performance and career development is strongly preferred.

• Hands-on experience designing and building AI, machine learning, or generative AI solutions, including retrieval-augmented generation, prompt engineering, model evaluation, orchestration, and production deployment considerations.

• Strong programming experience in SAS, R, and Python for clinical data analysis, automation, reporting, and statistical computing workflows.

• Demonstrated ability to translate business and scientific needs into technical designs, implementation plans, reusable frameworks, and reliable production capabilities.

• Experience delivering and supporting solutions in pharmaceutical, biotechnology, healthcare, or life sciences environments, with an understanding of regulated data, software validation, documentation, change control, and data integrity expectations.

• Ability to manage competing priorities, coordinate cross-functional dependencies, and guide projects from requirements through implementation, adoption, and ongoing support.

• Strong communication, problem-solving, and stakeholder management skills, with the ability to explain technical concepts, risks, limitations, and implementation tradeoffs to diverse audiences.

Preferred Skills:

• Experience managing a small engineering or programming team, including hiring, onboarding, performance management, development planning, and contractor oversight.

• Experience with data visualization, web application development, APIs, and user-facing analytics tools such as R Shiny, Power BI, Python dashboards, or custom web applications.

• Experience with clinical systems such as EDC, CTMS, eTMF, safety systems, data warehouses, or statistical computing platforms.

• Deep experience with SAS and R in clinical development, including data transformation, validation, analysis datasets, reporting, automation, and modernization of statistical programming workflows.

• Experience translating or refactoring code across SAS, R, Python, and SQL while maintaining analytical equivalence, auditability, and validation evidence.

• Experience designing AI agent architectures incorporating planning, tool calling, retrieval, guardrails, evaluation frameworks, human review, monitoring, and cost controls.

• Familiarity with cloud-based AI services, LLM APIs, vector databases, MLOps platforms, CI/CD practices, and secure deployment patterns.

• Knowledge of clinical trial data standards and workflows, including SDTM, ADaM, TFLs, data review, and regulatory submission support.

• Experience establishing technical standards, reusable solution patterns, documentation, training materials, and adoption practices.

• Experience with version control, secure software development, agile delivery, vendor evaluation, and resource planning.

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