AI Deployment Strategist

Role overview Own customer outcomes for deployments of a predictive AI product, from identifying valuable business decisions to ensuring model results are adopted in day-to-day workflows. You will work closely with customer leaders and a forward-deployed engineer: shaping the use case, success measures, and next steps while the engineering partner builds the solution. Customer-site travel is expected to exceed half of working time.

Responsibilities - Run discovery with business and data leaders to connect decision points with prediction opportunities, assess available data, and prioritize feasible, valuable use cases. - Write a deployment brief covering the business question, prediction target, data sources, baseline, success measure, and customer owner. - Define how value will be measured before development, then assess outcomes in business terms such as financial impact, time saved, or reduced risk. - Support adoption by integrating the model into customer workflows, training users, monitoring usage, and resolving barriers. - Maintain an account roadmap and prepare business cases for promising follow-on use cases. - Coordinate with the deployment engineer to unblock data, stakeholder access, and customer priorities; share recurring insights with product and research teams.

Requirements - Two to six years in a customer- or client-facing role with responsibility for outcomes, such as consulting, technical sales, applied data science, product, or early-stage operations. - Working familiarity with Python or SQL notebooks, prediction problems, and interpreting model backtests. - Strong communication and facilitation skills, including structuring ambiguous problems, writing concise briefs, and presenting to senior stakeholders. - Evidence of learning a new domain or technology quickly and delivering practical work. - Willingness to travel frequently, work closely with customers, and operate amid ambiguity. - A collaborative, humble approach and readiness to take on practical tasks that advance customer outcomes.

Nice to have - Industry knowledge in energy, financial services, insurance, shipping, retail, or private equity. - Exposure to applied machine learning, forecasting, or analytics products; a second business language.

Benefits and work setup - Base pay plus equity, medical, dental, and vision coverage, company equipment, and wellbeing, learning, and home-office stipends. - An eight-week onboarding program focused on the product, framing prediction problems, and measuring value.