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 working with and supporting different, cross-functional teams in a global, matrixed organization.
• Passionate Learner: Continuously explores emerging technologies, including generative AI, and identifies opportunities to apply them to drive innovation and business value.

AI Programming, Audience View, Biopharmaceutical Industry, Business Acumen, Business Intelligence (BI), Business Processes, Business Process Modeling, Database Design, Data Engineering, Data Literacy, Data Modeling, Data Science, Data Visualization, Extract Transform Load (ETL), Generative AI, Git Version Control System, Large Language Models (LLMs), Machine Learning (ML), Python (Programming Language), Software Development, Stakeholder Relationship Management, Version Control