Responsibilities

• Design, develop, and deploy statistical models and machine learning pipelines to drive product improvements, business decisions, and customer outcomes
• Work directly with customers during production pilots to build and deploy AI solutions that demonstrate measurable business value
• Design and execute A/B experiments and causal inference analyses to measure the impact of new features and model changes
• Build ROI models, business case tools, and forecasting systems for demand prediction, capacity planning, workforce optimization, and value quantification
• Apply NLP and generative AI techniques to extract insights from structured and unstructured data at scale, and partner with software engineers to productionize models with reliability, monitoring, and operational excellence
• Build and own customer analytics capabilities including segmentation (by size tier, AI adoption, product penetration, entitlement), usage trend analysis, propensity modeling, and foundational datasets combining service usage with sales data
• Create self-service analytics platforms and automated insight delivery mechanisms that enable leadership to pull strategic intelligence on demand
• Enable field teams with reusable analytical assets, diagnostic notebooks, benchmarking studies, and scalable tooling that accelerate customer engagements
• Own success metrics and create mechanisms to measure model performance, adoption, and business impact across customer cohorts
• Define strategic frameworks and GTM recommendations by segment, translating data patterns and market signals into actionable go-to-market motions and investment priorities
• Communicate findings and technical trade-offs to senior leadership and customer executives through written documents (6-pagers, science reviews) and presentations, operating as a shared resource across 2-3 teams simultaneously

Requirements

• PhD, or Master's degree and 6+ years of applied research experience
• 5+ years of building machine learning models for business application experience
• Experience with neural deep learning methods and machine learning
• Experience managing analytics, data science or technology teams, with a product or insight focus
• Experience working with diverse or differing data sets including creating and compiling data into a final distribution for management consumption
• Experience with customer segmentation, profiling, and targeting

Preferred

• PhD
• Track record of delivering end-to-end data science solutions from problem definition through production deployment

Benefits

• Diverse Experiences
• Inclusive Team Culture
• Mentorship & Career Growth
• Work/Life Balance