Associate Director, Data Science & AI Solutions

The mission of the Analytics & Technology Systems QA group is to provide the foundation to identify, explore, and efficiently develop solutions to enhance our quality oversight activities. Under the direction of the Senior Director, Analytics & Technology Systems of our Research & Development Division QA, we are seeking an Associate Director, Data Science & AI Solutions, to join our R&D Quality Assurance Analytics & Tech Systems team. In this role, you will bridge the gap between hands-on advanced software development and strategic business partnership. You will lead the design, deployment, and long-term lifecycle of our next-generation AI and data science solutions—ranging from traditional machine learning and simulations to custom generative AI applications (such as RAG and agentic workflows). As a technical and strategic leader, you will collaborate with business stakeholders to identify high-value opportunities, translate complex technical concepts for different audiences, and partner with governance bodies to ensure all advanced analytics tools are robust, compliant, and scalable.

1. AI Development & System Lifecycle Technical Delivery: Build, deploy, and maintain advanced AI applications (including custom RAG pipelines and agentic systems) alongside traditional analytics solutions (forecasts, simulations, and optimizations) to solve critical research & development quality business challenges. Environment Management: Work within enterprise analytics platforms (e.g., Dataiku, Posit Workbench) to monitor, enhance, and scale existing operational analytics systems. 2. GxP AI Governance & Compliance Regulatory Alignment: Collaborate with the GxP AI Governance Program to develop frameworks that ensure AI solutions adhere to regulatory requirements, validation playbooks, and standardized monitoring protocols. 3. Business Partnership & Technical Translation Translational Communication: Act as the primary technical translator, explaining complex AI algorithms, models, and limitations to non-technical stakeholders and leadership in a clear, business-friendly manner. Demand Intake: Collaborate with business leaders to identify operational challenges and propose high-value data science and AI use cases. Business Acumen: Demonstrates a strong willingness to learn and understand business processes, priorities, and stakeholder’s needs to drive meaningful outcomes. 4. Leadership & Upskilling Team Mentorship: Educate and upskill QA team members on modern tech stacks, Large Language Models (LLMs), and general data literacy. External Representation: Represent our company in cross-functional forums, such as IMPALA (Intercompany Quality Analytics).

Educations/Experience: BS/BA degree in relevant area and 5+ years of experience in the pharmaceutical, biotech or technology related industry. Analytical & Programming Core: Minimum 5 years of professional experience using Python, R, and advanced SQL to design and deploy machine learning, forecasting, or optimization models. Modern AI Engineering: Hands-on experience developing custom RAG architectures, working with LLM APIs, and utilizing core MLOps practices (version control/Git, monitoring, and model maintenance). Data Engineering & Visualization: Proficiency in designing user-friendly dashboards (PowerBI or Spotfire) and a foundational understanding of Extract, Transform, Load (ETL) pipelines and data structures. Cloud & Architecture: Familiarity with hosting and deploying AI solutions or agentic pipelines in cloud environments (AWS, Azure, or GCP). Regulated Industry Experience: Prior experience in a regulated pharmaceutical (GxP) environment, with exposure to software validation or AI governance frameworks. Communication & Influence: Exceptional communication skills with a proven ability to distill complex technical terminology into actionable insights for non-technical business partners. Global Collaboration: Experience