Lead Applied Scientist

Role overview

Scientific leadership and customer enablement

• Assess model performance, business impact, usability, robustness, and customer adoption; validate and operationalize research outcomes.
• Lead scientific work in high-impact sales and implementation engagements.
• Advise customers and internal stakeholders on configuring and applying AI solutions to complex business problems.
• Explain advanced machine learning, optimization, forecasting, pricing, and revenue management concepts to varied audiences.
• Address customer questions about solution design, methods, model outputs, and business impact.

Solution design and collaboration

• Analyze AI solutions for customer value and recommend configurations for customer objectives and industry use cases.
• Develop analytical frameworks, dashboards, and tools supporting implementation, adoption, and decision-making.
• Validate solution behavior, explain results, and maintain scientific rigor throughout implementations.
• Work with research, engineering, product, services, and sales teams to refine solutions and turn customer challenges into product and science improvements.
• Help define training, implementation methods, and best practices for broader self-service adoption.

Mentorship and thought leadership

• Mentor AI Research team members and provide scientific guidance across the organization.
• Contribute presentations, blogs, training materials, webinars, conferences, and other thought leadership.
• Represent PROS at customer events, conferences, and strategic industry engagements.
• Drive improvements that reduce implementation effort, shorten time-to-value, and improve customer outcomes.

Required qualifications

• Master's degree in a quantitative field such as Operations Research, Data Science, Machine Learning, Statistics, Applied Mathematics, Computer Science, or Industrial Engineering; PhD preferred.
• 10+ years in pricing, revenue management, forecasting, optimization, machine learning, offer optimization, or related decision science disciplines.
• Expertise in statistical modeling, machine learning, and applied AI; strong proficiency in Python, SQL, and Databricks.
• Direct customer, implementation, or consulting experience.
• Strong presentation, communication, and stakeholder management skills; able to explain advanced science to technical and executive audiences.
• Ability to influence cross-functional teams and strategic decisions.

Highly preferred experience

• Pricing, revenue management, forecasting, recommendation systems, or offer optimization experience.
• Experience with multiple AI areas, including forecasting, optimization, machine learning, or generative AI.
• Familiarity with reinforcement learning, recommendation systems, LLM applications, or agentic AI workflows.
• Enterprise software; dashboards and visualizations using Power BI, Tableau, or Databricks.
• Consulting, implementation, customer-success, solution architecture, or technical advisory experience.
• Thought leadership through presentations, publications, blogs, training, or industry engagement.

About PROS