AI-Native Outcomes & Value Analyst — Cambridge, MD

Description

AI-Native Outcomes & Value Analyst

CONTINGENT UPON ANTICIPATED JANUARY 2027 WORK

This position is contingent upon the company securing the anticipated work in January 2027. Candidate consideration may occur before the decision; however, any offer, start date, and onboarding depend on the work being secured and authorized to proceed. January 2027 is the anticipated work decision period, not a guaranteed start date.

Position Summary

A client-facing senior analyst who measures whether AI-native transformation delivers the results organizations need. This role integrates rigorous data analysis with stakeholder consulting and practical understanding of generative AI to establish baselines, evaluate human-AI workflows, quantify realized benefits, and turn findings into decisions and continuous improvement. Success is defined by demonstrated business outcomes—not dashboard volume.

Requirements

Key Responsibilities

• Lead client discovery sessions to translate strategic goals, operational challenges, and stakeholder questions into measurable outcomes, hypotheses, and analysis plans.

• Design measurement frameworks for AI-enabled workflows, human-AI collaboration, agentic or automated processes, workforce adoption, quality, and operational effectiveness.

• Establish reliable pre-implementation baselines, KPI definitions, target measures, comparison methods, and data-quality controls; document measurement assumptions and limitations.

• Analyze data with SQL and business intelligence tools such as Power BI; create accessible visualizations and concise executive-ready narratives to explain trends and tradeoffs.

• Apply generative AI and AI-assisted analytics appropriately, validating AI-produced summaries, calculations, classifications, and insights for accuracy, bias, privacy, and reproducibility.

• Quantify productivity, cycle-time, quality, cost, capacity, and adoption improvements; support business cases, benefits tracking, ROI assessments, and attribution of realized value.

• Partner with Agile delivery teams to embed iterative measurement in pilots, demonstrations, release reviews, feedback loops, and outcome-based improvement decisions.

• Communicate actionable recommendations to executives, delivery teams, and operational stakeholders, including where expected value is not being realized and what to adjust.

• Collaborate with data, product, operations, change management, and AI engineering teams to evaluate outcomes and sustain measurement after rollout.

Required Qualifications

• Bachelor’s degree in Data Analytics, Statistics, Computer Science, Information Systems, Business Analytics, or a related field, or equivalent relevant experience.

• 7+ years of professional data analytics, business intelligence, performance measurement, or value realization experience, including stakeholder-facing analytical work.

• Advanced SQL skills and experience with Power BI or comparable BI/data-visualization tools; ability to reconcile and validate data from multiple sources.

• Demonstrated experience developing KPIs, baselines, benefit measures, and analyses that translate into operational recommendations and leadership decisions.

• Practical experience using generative AI or AI-assisted analytics, with critical evaluation and verification of AI-generated outputs.

• Experience delivering analytics in an Agile or iterative delivery environment and adjusting measurement approaches based on stakeholder feedback.

• Strong analytical reasoning, communication, facilitation, executive presentation, and client collaboration skills.

Preferred Qualifications

• Experience analyzing AI adoption, automated or agentic workflows, human-in-the-loop processes, or AI-enabled operating models.

• Experience in a Scaled Agile Framework (SAFe) environment; SAFe Practice Consultant (SPC) certification preferred, not required.

• Experience with ROI models, benefits realization, experiment design, statistical methods, and/or Python or R.

• Experience supporting healthcare IT, federal government, or other regulated client environments.

• Familiarity with responsible AI, data governance, privacy, and organizational change/adoption measures.

Measures of Success

• Credible baselines and stakeholder-approved success measures exist for prioritized transformation initiatives.

• Actionable findings influence delivery decisions, workflow redesign, adoption actions, or investment priorities.

• Client leaders can see validated changes in productivity, cycle time, quality, adoption, efficiency, and realized business value.

Benefits are monitored after implement