Senior AI Analyst

JOB SUMMARY

The Senior AI Analyst is an experienced technical and analytical contributor who supports the evaluation, development, testing, adoption, and responsible use of artificial intelligence solutions. The role works with business and technology teams to translate operational needs into clearly defined AI use cases, analyze data and process outcomes, document requirements, and support proofs of concept and production solutions across Microsoft Azure, Microsoft Fabric, Power BI, Copilot Studio, and related enterprise platforms.

ESSENTIAL DUTTIES & RESPONSIBILITIES

• AI Use Case Analysis and Requirements
  • Partner with stakeholders to document business problems, desired outcomes, users, data needs, process impacts, risks, and success measures for proposed AI use cases.
  • Support use case intake, prioritization, feasibility assessment, and readiness analysis under established AI governance processes.
  • Create process maps, user stories, acceptance criteria, traceability documentation, test scenarios, and concise decision materials.

• AI Solution Prototyping and Evaluation

  • Assist with prototypes, proofs of concept, copilots, agents, and AI-assisted workflows using Microsoft Copilot Studio, Azure AI services, and approved SaaS-native AI capabilities.
  • Develop and test prompts, grounding approaches, retrieval scenarios, workflow logic, and user interactions under the direction of senior technical team members.
  • Evaluate solution outputs for accuracy, relevance, reliability, safety, bias, privacy, and alignment with defined business requirements; record results and recommend refinements.

• Testing, Adoption, and Operational Support

  • Coordinate user acceptance testing, capture defects and feedback, validate fixes, and maintain test evidence.
  • Create user guidance, training materials, release notes, knowledge articles, and adoption communications.
  • Monitor approved solution usage and performance indicators, identify recurring issues, and escalate risks or incidents through established support processes.

• Governance, Security, and Responsible AI

  • Follow enterprise architecture, cybersecurity, privacy, data governance, records management, and responsible AI requirements.
  • Use only approved data, environments, and AI tools; protect confidential and personal information and promptly report concerns.

• Collaboration and Continuous Learning

  • Work closely with AI, data, application development, architecture, security, technical delivery, and functional business teams.
  • Participate in design reviews, sprint activities, demonstrations, retrospectives, and knowledge-sharing sessions.
  • Build proficiency through structured coaching, hands-on assignments, formal training, and documented development goals.

QUALIFICATIONS

• Master’s degree in Data Science, Computer Science, Artificial Intelligence, Machine Learning, Business Administration and Analytics, Information Systems, Statistics, Applied Mathematics or Finance, or Engineering.
• 2+ years of relevant professional, internship, academic, or project experience in data analysis, AI, analytics or policy, software, automation, or technology consulting.
• Foundational experience with one or more of the following: SQL, Excel, Power BI, Microsoft Fabric, Azure, Copilot Studio, machine learning, natural language processing, or workflow automation.
• Ability to analyze data, document assumptions, test outputs, recognize data quality issues, and communicate findings accurately.
• Familiarity with generative AI concepts, prompt design, retrieval-augmented generation, model evaluation, or responsible AI practices is preferred.
• Strong written and verbal communication skills in English, with the ability to explain analytical and technical findings in business-friendly language.
• Curiosity, sound judgment, attention to detail, and a willingness to learn through feedback and structured supervision.
• Ability to organize work, manage priorities, document progress, and collaborate across technical and nontechnical teams.

ROLE COMPETENCIES

• Analytical Thinking: Uses evidence, structured reasoning, and clearly documented assumptions to evaluate problems and recommendations.
• Applied AI Learning: Builds practical knowledge of AI, data, automation, and enterprise technology through supervised assignments and continuous learning.
• Quality and Data Stewardship: Supports accurate, c…