Description

  AI & Advanced Analytics

• Build machine learning models, predictive analytics solutions, NLP tools, and AI-powered automations.
• Lead end-to-end model development, including data acquisition, feature engineering, training, validation, deployment, and monitoring.
• Apply responsible AI frameworks to ensure fairness, transparency, explainability, and accountability.

Data Strategy & Governance

• Maintain data quality standards, metadata practices, and robust model documentation.
• Partner with IT and Cybersecurity teams to ensure secure handling of sensitive and regulated data.
• Support development of AI ethics guidelines, model risk management practices, and enterprise data governance initiatives.

Cross-Department Collaboration

• Identify opportunities to use AI to improve services, reduce costs, and strengthen community outcomes.
• Translate complex analytics into clear, actionable insights for executive leadership and non-technical audiences.
• Provide mentorship and technical guidance to analysts and departmental partners.

Reporting & Visualization

• Develop dashboards, automated reports, and data visualizations that communicate trends, forecasts, KPIs, and performance metrics.
• Integrate AI-generated insights into reporting systems to support real-time decision-making and operational readiness.
• Develop and maintain Business Intelligence solutions (e.g., Power BI) that support transparency, operational readiness, and executive briefings.

Scheduled Milestones and Deliverables Required

• Operational AI solutions in production improving at least three resident-facing services or internal processes (e.g., service triage, demand forecasting, permit processing times).
• A functioning Responsible AI governance workflow, including model documentation, bias testing, human-in-the-loop review, and incident response procedures.
• Executive-ready dashboards for priority KPIs (service delivery, equity indicators, fiscal efficiency) with near real-time insights integrated.
• Established data quality standards and metadata practices supporting MLOps pipelines.
• A sustainable cross-department engagement model (intake → discovery → delivery → monitoring), including skills uplift and mentorship initiatives.

Metrics to be Utilized to Measure the Performance of this Resource Required

Model Delivery Metrics

• Number of AI/ML models delivered (pilot, production, or enhancement).
• Average time from project intake to pilot deployment.
• Percentage of models meeting defined performance thresholds (e.g., MAE, F1, AUROC).

Model Quality & Performance

• Frequency of model drift or degradation incidents.
• Time to identify and remediate model drift (target: within established SLA).
• Success rate of model retraining without regression or fairness issues.

Innovation & Complexity

• Number of solutions utilizing advanced techniques (LLMs, NLP, geospatial analytics, automation).
• Reuse rate of modular components (features, pipelines, datasets)

Key Skills

Education

Bachelor's degree