Associate Director, Commercial AI Tech Lead

The Associate Director, Commercial AI Technical Lead provides seniorlevel technical expertise and handson 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 valuedriven. The role is responsible for supporting build versus buy evaluations, assessing the quality, robustness, and longterm 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.

• Support the responsible application of advanced analytics, machine learning, and generative AI techniques in commercial contexts.

• Partner closely with Commercial, DEC Data Engineering, Architecture, and Security teams and Privacy and Compliance teams to support delivery of scalable, secure AI solutions.

• Translate complex technical concepts into clear, actionable guidance for non-technical stakeholders.

• Ensure commercial AI solutions align with enterprise data, AI, security, and governance standards.

Required Knowledge, Skills, and Abilities

• Solid understanding of machine learning techniques, model evaluation, and real world deployment considerations.

• Experience evaluating third party AI solutions and comparing them with internally developed approaches.

• Experience working in regulated environments with awareness of privacy, compliance, and explainability requirements.

• Strong communication and collaboration skills with the ability to influence without formal authority.

• Experience supporting AI initiatives in a matrixed enterprise environment.

• Experience working with commercial data sources such as CRM, claims, promotional, and digital engagement data.

Required/Preferred Education and Licenses

• Bachelor's degree with advanced training or experience in Data Science, Statistics, Computer Science, Engineering, Social science fields (quantitative psych, quantitative sociology, or economics) or a related quantitative field; advanced degree preferred.

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