Associate Director, Commercial AI Tech Lead

If you are a current Jazz employee please apply via the Internal Career site

Jazz Pharmaceuticals is a global biopharma company whose purpose is to innovate to transform the lives of patients and their families. We are dedicated to developing life-changing medicines for people with serious diseases — often with limited or no therapeutic options. We have a diverse portfolio of marketed medicines, including leading therapies for sleep disorders and epilepsy, and a growing portfolio of cancer treatments. Our patient-focused and science-driven approach powers pioneering research and development advancements across our robust pipeline of innovative therapeutics in oncology and neuroscience. Jazz is headquartered in Dublin, Ireland with research and development laboratories, manufacturing facilities and employees in multiple countries committed to serving patients worldwide. Please visit www.jazzpharmaceuticals.com for more information.

Job Description

The Associate Director, Commercial AI Technical Lead provides senior‑level technical expertise and hands‑on leadership across AI and advanced analytics initiatives for the Commercial function of Jazz. This role acts as a key individual contributor and technical advisor, partnering closely with the Commercial organization and Digital Enterprise Capabilities Data & AI colleagues to ensure AI solutions are technically sound, scalable, compliant, and value‑driven. The role is responsible for supporting build versus buy evaluations, assessing the quality, robustness, and long‑term viability of AI solutions, and contributing to the prioritization of commercial AI use cases based on business impact, feasibility, data readiness, and risk. Strong applied data science skills and pharmaceutical commercial experience are critical to success in this role. 

​​Essential Functions/Responsibilities 

• ​Apply data science expertise for AI and advanced analytics solutions across Commercial use cases such as forecasting, targeting, segmentation, personalization, omnichannel engagement, and commercial optimization. 

• ​Provide technical expertise in Generative AI applications for field teams (e.g., pre-call planning), marketing teams (e.g., content generation or market research), operations teams (e.g., knowledge summarization and search), etc. 

• ​Evaluate the technical quality of AI solutions, including (where applicable) LLM strategy, solution architecture, model design, featurization, data requirements, validation approaches, explainability, monitoring, and scalability. 

• ​Review and challenge analytical approaches, assumptions, and outputs from internal teams and external vendors to ensure solutions meet Jazz standards for rigor, trust, and compliance. 

• ​Support technical assessments of build versus buy options for commercial AI capabilities, considering time to value, cost, scalability, extensibility, vendor dependency, and long term ownership. 

• ​Participate in technical due diligence of vendor solutions, including review of methodologies, data usage, solution transparency, and operational readiness. 

• ​Contribute clear technical input and recommendations to inform investment and solution implementation decisions.  Evaluate build versus buy frameworks. 

• ​Support prioritization of commercial AI initiatives by assessing feasibility, data readiness, implementation complexity, adoption readiness, and potential business impact. 

• ​Partner with commercial and analytics stakeholders to define success metrics and ensure AI solutions are designed to deliver measurable value. 

• ​Define pilot or MVP solutions that test solution applicability, confirm value measurement and define supporting process changes. 

• ​Identify opportunities to reuse, standardize, or scale AI capabilities across brands, markets, or commercial functions. 

• ​Remain hands on as needed, contributing to or reviewing development, experimentation design, feature engineering, and analytical pipelines. 

• ​Apply best practices in data science, including model validation, bias assessment, explainability, and documentation appropriate for a regulated environment.