Associate Director, Forward Deployed Decision Science

About the Role

Do you want to be the person the business calls when a hard commercial question needs a real answer? In this forward-deployed role, you will sit side by side with Alexion's commercial leaders to understand their challenges first-hand, shape them into analytical and AI-enabled solutions, and bring the answers back in clear, practical language that drives decisions and action.

This is a business-facing Decision Science role - You will own the connection between the business problem and the analytics that solve it.

What You'll Do

• Partner directly with the business: Embed with indication teams, Brand Marketing, Sales Operations, Insights, sales leadership, RDUs, and RAMs to understand their needs and priorities.
• Translate needs into solutions: Convert business questions into the right mix of data science, machine learning, analytical models, AI-enabled solutions, or decision frameworks.
• Deliver measurable outcomes: Develop and apply analytical and machine learning approaches to commercial problems such as sales effectiveness, targeting, patient finding, promotional investment, and other relevant business use cases, and own the connection to business outcomes.
• Make the complex simple: Turn sophisticated analytical output into clear, jargon-free recommendations that stakeholders can act on with confidence.
• Lead with agentic AI: Design and orchestrate AI agents and human-agent workflows that accelerate analysis and decision-making, applying current AI-native approaches alongside established analytical methods.
• Build on the cloud: Use cloud platforms such as Databricks, AWS, Snowflake, Azure, or comparable environments as the foundation for scalable and efficient analytical solutions.
• Bring fresh thinking: Operate independently, challenge the status quo, and bring new ideas and expertise that can strengthen the team's capabilities.
• Safeguard trust: Apply sound statistical judgment and validate AI-generated outputs while working within data privacy requirements, de-identified data practices, and applicable AZ AI governance standards.

What You'll Bring

Essential

• Education: Master's degree or PhD in Data Science, Statistics, Biostatistics, Engineering, Economics, Applied Mathematics, or a related quantitative field.
• Relevant experience: Typically 4+ years of experience applying decision science, data science, advanced analytics, or quantitative methods to real business problems. Strong and recent experience with modern AI and analytics is more important than total years alone.
• Demonstrated business impact: Track record of applying analytics in a commercial environment such as sales effectiveness, commercial operations, brand marketing, or a relevant adjacent area such as supply chain.
• Cloud computing: Hands-on experience with Databricks, AWS, Snowflake, Azure, or a comparable cloud/data platform. Exact platform experience can vary.
• AI-native and agentic workflows: Practical, recent experience using AI-enabled approaches, coding agents, or agentic workflows, including human-agent interaction and orchestration.
• Technical foundation: Working proficiency in Python and SQL, with practical experience in machine learning, predictive modeling, statistical analysis, and model validation. Depth may vary by model family and use case; mastery of every method or library is not required.
• Stakeholder management: Strong communication and consulting skills, with demonstrated ability to engage non-technical leaders, simplify complex topics, and influence decisions without relying on technical jargon.
• Independent delivery: Ability to make an impact quickly, operate with autonomy, and bring new expertise rather than requiring significant training to become effective in the role.

Desirable

• Healthcare or life sciences: Experience in pharmaceutical, biotechnology, healthcare, life sciences, rare disease, or a healthcare practice within consulting or technology.
• Commercial data: Familiarity with commercial healthcare data such as claims, CRM, specialty pharmacy, patient services, or comparable datasets.
• Commercial analytics use cases: Experience with areas such as marketing mix modeling, next-best action, segmentation, forecasting, patient identification, adherence, or field effectiveness.
• Visualization: Experience with Power BI, Tableau, Qlik, or comparable tools for communicating analytical findings and enabling business decisions.

Why This Role Matters

Your work will help teams use data, advanced analytics, and AI more effectively to identify opportunities, improve commercial decision-making, and focus resources where they can have the greatest impact for patients living with rare diseases.