Associate Director, Analytical AI Forecasting and Predictive Solutions

Role Summary

The Associate Director, Analytical AI Forecasting and Predictive Solutions leverages technical expertise to develop and deploy cutting-edge data science and machine learning solutions across all Forecasting applications, with a focus primarily on Commercial Demand. This role is pivotal in transforming enterprise forecasting capabilities, shaping strategies, and implementing advanced methodologies, processes, and technologies that will accelerate insight generation and enhance decision-making and planning across the organization. Location: Princeton, NJ, US.

Responsibilities

• Contribution to Analytical AI Community
  • Contribute as part of a dynamic group of data scientists, machine learning engineers, and forecasting SMEs.
  • Help inspire and shape the organizational mindset to leverage forecasting as a strategic tool for identifying risks and opportunities, driving impactful decision-making and planning.
• Development, implementation, maintenance, and analysis of AAI Forecasting Solutions
  • Contribute to the build of advanced forecasting models using statistical, machine learning, and AI techniques.
  • Lead analyses of large datasets to identify trends, patterns, and insights that inform forecasting models.
  • Collaborate with cross-functional teams, including finance, marketing, and operations, to understand business needs and translate them into data-driven forecasting solutions.
  • Continuously monitor and improve the accuracy and performance of forecasting models.
  • Communicate complex data insights and forecasting results to stakeholders in a clear and actionable manner.
  • Design and conduct experiments to validate model assumptions and improve forecasting accuracy.
  • Develop and maintain documentation for forecasting models, processes, and procedures.
  • Provide mentorship and guidance to junior data scientists and analysts within the team.
• Continuous Innovation
  • Stay up-to-date with latest advancements in data science, machine learning, and forecasting methodologies; seek new opportunities to deploy next-generation capabilities that drive high impact across the enterprise.
• Best Practices Implementation
  • Partner with stakeholders to facilitate and implement best practices in forecasting, data science, and machine learning.
  • Continuously monitor the external data science landscape to identify and apply new capabilities that support technological evolution and business performance.
• Cross-Functional Collaboration
  • Work collaboratively with analytics and business stakeholders to propose and implement innovative methodologies, process improvements, and technology solutions.
  • Engage with Finance, Commercial, Market Access, and Analytics teams in the preparation, presentation, and discussion of forecasting results.
• Performance Monitoring and Analytics
  • Contributes to the performance and impact assessments of newly deployed capabilities across the portfolio.

Qualifications

• Minimum education of a BA/BS in a quantitative field required; MBA or other graduate degree preferred.
• Minimum of seven (7) years of hands-on experience in advanced analytics and data science. Hands-on experience with pharmaceutical forecasting is preferred.
• Proficiency in Python/R, SQL, Tableau, and MS Office Suite. Experience with Git and cloud-based environments (AWS, Azure, etc.) and/or experience in commercial forecasting is preferred.
• Excellent communication and presentation skills, with a proven ability to explain complex analyses and outcomes to both technical and non-technical stakeholders.
• Proven experience in operating large, complex projects with multi-functional team members.

Education

• BA/BS in a quantitative field required; MBA or other graduate degree preferred.

Skills

• Data science, machine learning, forecasting techniques
• Statistical modeling and analysis of large datasets
• Forecasting development, validation, and deployment
• Communication of complex results to technical and non-technical audiences
• Cross-functional collaboration and project leadership