Senior Technical & Functional Analyst (AI / Automation)

Ariel Partners•Nyc, NY

14h•Onsite

About The Position

SNAP Payment Error Rate (CAP) reduction initiative is a top-priority, agency-wide strategic effort aimed at mitigating federal oversight findings and avoiding substantial financial penalties amounting to millions of dollars. In partnership with McKinsey, the agency is leveraging artificial intelligence, Robotic Process Automation and advanced analytics to strengthen eligibility, case processing accuracy, and quality control review. This initiative will modernize error detection, introduce proactive prevention capabilities, and enhance operational decision-making through data-driven insights. The outcome is expected to reduce payment inaccuracies, accelerate case resolution, improve compliance, and increase public trust in program integrity.

Requirements

Minimum 10 years of experience in functional and technical analysis across enterprise applications and data-driven solutions.
Minimum 5 years of strong understanding of AI/ML concepts, automation frameworks, and data fundamentals.
Minimum 10 years of experience preparing BRDs, FSDs, user stories, workflow diagrams, and system documentation.
Minimum 7 years of experience analyzing structured and unstructured data and interpreting results to support business decisions.
Minimum 7 years of hands-on experience writing complex SQL, optimizing queries, and working with enterprise RDBMS platforms (e.g., SQL Server, Oracle, PostgreSQL).
Minimum 5 years of experience working with enterprise data warehouses, ETL/ELT processes, and data modeling.
Minimum 5 years of experience applying statistics and ML techniques on real-world datasets, including model validation and iteration.
Minimum 5 years of exposure with data libraries (e.g., Python, pandas) and exposure to CI/CD or MLOps practices.
Minimum 5 years of experience evaluating AI tools, frameworks, or vendors and making solution or architecture recommendations.
Minimum 7 years of experience leading or overseeing pilots/POCs, defining success metrics, and contributing to scale-up or roadmap planning.

Responsibilities

Lead workshops with business stakeholders to document business processes, pain points, user stories, functional specifications, and acceptance criteria.
Perform feasibility analysis to identify opportunities for AI/ML, automation, decisioning, and workflow optimization.
Develop process maps (BPMN), system flows, data lineage, and integration documentation.
Translate business requirements into clear, actionable technical specifications, including APIs, data flows, validation rules, and model inputs/outputs.
Analyze existing applications, databases, integrations, and AWS cloud environments to inform solution design and implementation.
Collaborate closely with architects, developers, data scientists, and engineers to ensure accurate interpretation of requirements and solution intent.
Work with data science and engineering teams to define data needs, metrics, business rules, validation logic, and AI/ML model behavior.
Lead and support AI pilots and POCs, including defining success metrics, tracking outcomes, and documenting lessons learned for scale-up.
Conduct root cause analysis and recommend improvements to accuracy, efficiency, compliance, and user experience.
Support dashboarding, analytics KPIs, and reporting for business and executive leadership.
Support User Acceptance Testing (UAT), traceability, defect triage, and business sign-offs.
Partner with QA teams to ensure robust testing coverage across multiple business and edge scenarios, especially for AI-driven solutions.
Act as a liaison between business program areas, IT delivery teams, vendor partners, and technical SMEs to ensure alignment and clarity.
Produce regular status updates, technical documentation, and executive-level summaries on progress and outcomes.
Support training, SOP updates, knowledge transfer, and production rollouts.