Data Analyst with AI

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          "text": "<p><strong>Sr. Data Analyst<br>\nLocation: Houston, TX 77002<br>\nContract</strong></p><p><br>\nWork /Schedule: 8am-5pm, Hybrid (3 days in office, 2 days WFH)<br>\nPPA #: 13644</p><p>&#xa0;</p><p><strong>Project and Requirements&#xa0;Required</strong></p><p>The Senior Data Analyst (AI) leads the development, deployment, and governance of advanced analytics and artificial intelligence solutions that drive data-informed decision-making.</p><p>This role helps the organization use data and AI responsibly to improve resident services, increase operational efficiency, strengthen community trust, and support evidence-based policymaking. The work produced directly impacts service quality, transparency, and the Client’s ability to deploy AI safely and effectively.</p><p>The Senior Data Analyst (AI) blends deep analytical expertise with a practical understanding of public sector operations, ensuring AI tools are ethical, transparent, secure, and aligned with community priorities. This position is instrumental in modernizing government operations and advancing enterprise AI capabilities.</p><p><strong>Role and Responsibilities of the Resource Request&#xa0;Required</strong></p><p><strong>AI &amp; Advanced Analytics</strong></p><ul><li>Build machine learning models, predictive analytics solutions, NLP tools, and AI-powered automations.</li><li>Lead end-to-end model development, including data acquisition, feature engineering, training, validation, deployment, and monitoring.</li><li>Apply responsible AI frameworks to ensure fairness, transparency, explainability, and accountability.</li></ul><p><strong>Data Strategy &amp; Governance</strong></p><ul><li>Maintain data quality standards, metadata practices, and robust model documentation.</li><li>Partner with IT and Cybersecurity teams to ensure secure handling of sensitive and regulated data.</li><li>Support development of AI ethics guidelines, model risk management practices, and enterprise data governance initiatives.</li></ul><p><strong>Cross-Department Collaboration</strong></p><ul><li>Identify opportunities to use AI to improve services, reduce costs, and strengthen community outcomes.</li><li>Translate complex analytics into clear, actionable insights for executive leadership and non-technical audiences.</li><li>Provide mentorship and technical guidance to analysts and departmental partners.</li></ul><p><strong>Reporting &amp; Visualization</strong></p><ul><li>Develop dashboards, automated reports, and data visualizations that communicate trends, forecasts, KPIs, and performance metrics.</li><li>Integrate AI-generated insights into reporting systems to support real-time decision-making and operational readiness.</li><li>Develop and maintain Business Intelligence solutions (e.g., Power BI) that support transparency, operational readiness, and executive briefings.</li></ul><p><strong>Scheduled Milestones and Deliverables&#xa0;Required</strong></p><ul><li>Operational AI solutions in production improving at least three resident-facing services or internal processes (e.g., service triage, demand forecasting, permit processing times).</li><li>A functioning Responsible AI governance workflow, including model documentation, bias testing, human-in-the-loop review, and incident response procedures.</li><li>Executive-ready dashboards for priority KPIs (service delivery, equity indicators, fiscal efficiency) with near real-time insights integrated.</li><li>Established data quality standards and metadata practices supporting MLOps pipelines.</li><li>A sustainable cross-department engagement model (intake → discovery → delivery → monitoring), including skills uplift and mentorship initiatives.</li></ul><p><strong>Metrics to be Utilized to Measure the Performance of this Resource&#xa0;Required</strong></p><p><strong>Model Delivery Metrics</strong></p><ul><li>Number of AI/ML models delivered (pilot, production, or enhancement).</li><li>Average time from project intake to pilot deployment.</li><li>Percentage of models meeting defined performance thresholds (e.g., MAE, F1, AUROC).</li></ul><p><strong>Model Quality &amp; Performance</strong></p><ul><li>Frequency of model drift or degradation incidents.</li><li>Time to identify and remediate model drift (target: within established SLA).</li><li>Success rate of model retraining without regression or fairness issues.</li></ul><p><strong>Innovation &amp; Complexity</strong></p><ul><li>Number of solutions utilizing advanced techniques (LLMs, NLP, geospatial analytics, automation).</li><li>Reuse rate of modular components (features, pipelines, datasets).</li></ul>",
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