Sr. Applied Scientist – AI Velocity Team, Applied AI Acceleration Solutions Architecture

About the role

As a Senior Applied Scientist within the Applied AI Solutions team, you will collaborate across AI Velocity Teams (AIVT), enabling multiple customer engagements simultaneously. You will lead data science initiatives that span the full lifecycle — from identifying high-value business problems and formulating hypotheses, through rigorous experimentation and modeling, to deploying production-grade solutions that serve thousands of customers.

Responsibilities

• Design, develop, and deploy statistical models and machine learning pipelines to drive product improvements and business decisions
• Work directly with customers during production pilots to design, build, and deploy AI solutions that demonstrate measurable business value
• Work with Applied AI Solutions Architects and Customer Success Specialists to design, build, and deploy AI solutions in customer environments during fixed deployment cycles
• Enable field teams with data-driven insights, reusable analytical assets, ROI tools, and scalable tooling that accelerate customer engagements and solution delivery
• Own success metrics and create mechanisms to measure model performance, adoption, and business impact
• Communicate findings and technical trade-offs to senior leadership and customer executives through written documents (6-pagers, science reviews) and presentations
• Operate as a shared resource across 2-3 AIVT teams simultaneously, providing data science expertise across multiple customer engagements

Qualifications

• Master's degree in engineering, statistics, computer science, mathematics, or a related quantitative field
• 5+ years of quantitative and qualitative data science/business intelligence with significant business impact experience
• 3+ years of machine learning, statistical modeling, data mining, and analytics techniques experience
• PhD, or PhD and 4+ years of designing experiments and statistical analysis of results experience
• Experience in A/B testing
• Proficiency in Python and SQL; experience with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost
• Track record of delivering end-to-end data science solutions from problem definition through production deployment