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Data Analyst; AI

Job in Duluth, St. Louis County, Minnesota, 55806, USA
Listing for: BuzzClan
Full Time position
Listed on 2026-06-01
Job specializations:
  • IT/Tech
    AI Engineer, Data Analyst
Salary/Wage Range or Industry Benchmark: 80000 - 100000 USD Yearly USD 80000.00 100000.00 YEAR
Job Description & How to Apply Below
Position: Data Analyst (AI)

Role:
Data Analyst (AI)

Location:

Houston, TX 77002 (Hybrid: 3 days in office / 2 days WFH)

Duration: 12 months

Role Overview

The Data Analyst (AI) leads the development, deployment, and governance of advanced analytics and AI solutions. This role modernizes government operations by blending deep technical expertise with practical public‑sector applications, ensuring AI tools are ethical, secure, and data‑informed.

Key Responsibilities
  • AI & Advanced Analytics: Build end‑to‑end ML models, NLP tools, and predictive analytics pipelines (from data acquisition through to deployment and monitoring) using responsible AI frameworks.
  • Data Strategy & Governance: Maintain MLOps data quality standards, document models, and partner with IT/Cybersecurity to secure sensitive data.
  • BI & Visualization: Build Power BI dashboards and automated reports to translate complex AI insights into real‑time metrics for executive leadership.
  • Cross‑Functional

    Collaboration:

    Partner with departments to identify AI opportunities, mentor junior analysts, and improve resident‑facing services.
Key Deliverables & Milestones
  • Production AI Solutions: Deploy AI tools to optimize at least 3 internal or resident‑facing processes (e.g., triage, forecasting, permit processing).
  • AI Governance Framework: Establish bias testing, model documentation, and incident response workflows.
  • Executive Dashboards: Launch near‑real‑time KPI dashboards focused on service delivery and fiscal efficiency.
Success Metrics
  • Delivery: Number of models deployed; project intake‑to‑pilot duration; model threshold targets (MAE, F1, AUROC).
  • Quality: Frequency and time‑to‑remediation of model drift; successful retraining without bias/regression.
  • Innovation: Effective use of advanced tech (LLMs, NLP, geospatial) and high reusability of pipeline components.
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