Data Scientist

Role overview Sit at the crossroads of strategy and engineering for every client engagement. Partner with solutions architects during the discovery phase, construct the ROI models that anchor each business case, and own the evaluation and measurement layer behind every production AI system. The position blends applied analytics with hands-on evaluation work and direct client communication.

Responsibilities - Analyze client workflows and operational datasets to surface the highest-leverage AI opportunities. - Build ROI models, FTE analyses, and scenario forecasts that shape build priorities and business cases. - Design and run evaluations for production AI systems—covering accuracy, cost, latency, and downstream business-metric outcomes. - Establish baseline metrics and track post-deployment performance across the installed client base. - Contribute patterns, benchmarks, and methodology improvements to the internal applied-AI framework.

Requirements - Strong applied data science or analytics background, comfortable working with messy operational data rather than only clean datasets. - Experience modeling business outcomes and translating analysis into executive-grade narratives. - Hands-on experience with LLM evaluations, RAG benchmarking, or broader AI system performance measurement. - Excellent communication skills, including presenting to client leadership teams.

Nice to have - Domain expertise in healthcare, legal, financial services, or other operationally complex industries.

Benefits and work setup Competitive compensation, a fully remote and async-friendly team, and direct exposure to client leadership teams across more than ten industries. The role offers the chance to define how applied AI is measured at scale.