Edtech.com's Summary

University of Connecticut is hiring an IT-SLE Operations Analyst. The analyst bridges business intelligence, data analysis, and AI adoption for operational teams, applying automation and advanced analytics to surface trends and support decisions. Day to day work spans prototyping AI-assisted workflows, validating model outputs, and explaining findings to non-technical stakeholders.

Highlights

Assess operational workflows and identify where AI, automation, and advanced analytics can improve efficiency and decision-making.
Perform data analysis and modeling using large datasets to uncover trends and support operational decisions.
Prototype and test AI-enabled analytical methods that augment existing dashboards and reporting.
Build low-code or no-code automations to streamline data preparation and reporting tasks.
Validate AI outputs for accuracy, bias, and relevance before they're used in decision-making.
Explain AI-supported insights and modeling results to stakeholders in clear, non-technical language.
Document AI-assisted workflows and methodologies to keep analysis transparent and reproducible.
Bachelor's degree in information technology, business administration, data analysis, or a related field, plus six years of related experience.
Proficiency with at least one scripting or automation tool such as Python, SQL, PowerShell, or R.

IT-SLE Operations Analyst Full Description

Job Summary

Serves as the bridge between business intelligence, data analysis, advanced analytics, and innovative AI solutions, incorporating AI into daily analytical processes and decision-making for operational teams. Performs data analysis and modeling to identify trends, patterns, opportunities, and solutions to operational and business problems, while leveraging artificial intelligence tools, automation, and advanced analytics to improve data exploration, statistical models, generate actionable insights, and support decision-making. This position leverages the practical integration of AI into existing operational workflows.

Duties and Responsibilities

Assess and Identify Areas of Opportunity for AI

Identify the standard operational workflows, systems, and processes for an operational team and assess opportunities where AI may support efficiencies in workflow, data collection, analysis, and decision-making.
Assess operational workflows, systems, and analytical processes to identify inefficiencies and opportunities for AI, automation, and process improvement, reducing manual effort, improving turnaround time, or enhancing analytical consistency.
Perform ad hoc and recurring data analysis and modeling to address operational questions, identify trends and patterns, evaluate performance, and support decision-making.
Collect, prepare, evaluate, and analyze data from multiple sources to identify opportunities, assess operational performance, and develop actionable insights.
Meet with operational partners to determine feasibility of applying AI‑assisted tools (e.g., copilots, large language models, automated analytics) to support data exploration, modeling, insight generation, and analytical efficiency.

Develop and Implement AI Solutions

Partner with analysts to introduce AI‑enabled methods into standard analytical practices.
Translate business questions into AI‑assisted analytical approaches, including natural‑language querying, statistical analysis, model interpretation, pattern detection, and predictive or other analytical modeling.
Prototype and test AI‑enabled analytical methods that augment existing reporting, dashboards, and analysis workflows.
Develop low‑code or no‑code automations within existing software that streamline routine data preparation, analysis, and reporting tasks.
Apply and interpret statistical, mathematical, or predictive models, as appropriate, to support operational planning, evaluation, forecasting, and decision-making.
Collaborate with data engineers or platform teams to operationalize repeatable analytical processes.
Validate AI outputs for accuracy, bias, and relevance before use in decision‑making.
Integrate AI-supported reporting, trend detection, anomaly identification, and analytical modeling to support dashboards, reports, and ad‑hoc analyses.

Communication and Stewardship of AI Solutions

Collaborate with stakeholders to explain AI‑supported insights, analytical findings, and modeling results in clear, non‑technical language.
Serve as a resource for responsible AI use, helping analysts understand appropriate use cases and limitations.
Follow organizational standards for data governance, privacy, and responsible AI use.
Document AI‑assisted workflows, analytical methodologies, modeling assumptions, and sources to ensure transparency and reproducibility.
Stay informed on emerging AI tools and techniques relevant to data analysis and modeling, recommending adoption when value‑aligned.
Supports technical projects as part of a project team.
Evaluate internal systems for efficiency, problems, and inaccuracies.
Performs related work as required.

Minimum Qualifications

Bachelor's degree in information technology, business administration, data analysis or related field, and six years of related experience.
Experience applying AI tools to improve real-world workflows (e.g., analysis, drafting, summarization, customer support, knowledge search, or automation).
Experience performing statistical data analysis and modeling using large datasets, with practical experience in understanding their use in making informed decisions.
Experience evaluating and operating AI solutions (test cases/metrics, monitoring/logging) with a strong grounding in responsible AI (privacy, security, bias, accessibility).
Experience taking an AI-enabled workflow from prototype to pilot/production, including stakeholder feedback, documentation, and basic automation/API integration.
Ability to design, iterate, and document prompts/workflows; validate AI outputs for accuracy, bias, and appropriateness before use.
Ability to translate stakeholder needs into clear problem statements, success criteria, and practical AI-enabled solutions.
Working knowledge of AI/ML and generative AI fundamentals, including common limitations and risks (e.g., model evaluation basics, hallucinations, and prompt sensitivity).
Proficiency with at least one scripting or automation approach (e.g., Python, SQL, PowerShell, R, or a low-code platform) to streamline and standardize processes.
Knowledge of responsible AI, privacy, and security practices, including appropriate handling of sensitive data when using AI systems.
Knowledge of statistical models, data analysis techniques, predictive modeling, and/or optimization.
Experience communicating analytical findings to non-technical stakeholders.

Preferred Qualifications

Hands-on experience building AI-assisted solutions with large language models (LLMs), including prompt/workflow design and structured outputs.
Familiarity with retrieval-augmented generation (RAG), semantic search, embeddings, and working with unstructured content (documents/knowledge bases).
Experience applying advanced analytical, statistical, predictive, or mathematical modeling techniques to evaluate operational performance, identify trends and patterns, and develop data-driven recommendations.

Appointment Terms

This is a full-time, permanent position. The University offers a competitive salary, and outstanding benefits, including employee and dependent tuition waivers at UConn, and a highly desirable work environment. For additional information regarding benefits, please visit UConn Health Benefits and explore the sections under the Benefits & Leaves and Engagement & Learning headers. Other rights, terms, and conditions of employment are contained in the collective bargaining agreement between the University of Connecticut and the University of Connecticut Professional Employees Association (UCPEA).

Terms and Conditions of Employment

Employment of the successful candidate is contingent upon the successful completion of a pre-employment criminal background check.

All employees are subject to adherence to the State Code of Ethics.

The University of Connecticut is an AA/EEO employer including for Disability and Veteran status.

Original Job Description

Job Summary

Duties and Responsibilities

Assess and Identify Areas of Opportunity for AI

Identify the standard operational workflows, systems, and processes for an operational team and assess opportunities where AI may support efficiencies in workflow, data collection, analysis, and decision-making.
Assess operational workflows, systems, and analytical processes to identify inefficiencies and opportunities for AI, automation, and process improvement, reducing manual effort, improving turnaround time, or enhancing analytical consistency.
Perform ad hoc and recurring data analysis and modeling to address operational questions, identify trends and patterns, evaluate performance, and support decision-making.
Collect, prepare, evaluate, and analyze data from multiple sources to identify opportunities, assess operational performance, and develop actionable insights.
Meet with operational partners to determine feasibility of applying AI‑assisted tools (e.g., copilots, large language models, automated analytics) to support data exploration, modeling, insight generation, and analytical efficiency.

Develop and Implement AI Solutions

Partner with analysts to introduce AI‑enabled methods into standard analytical practices.
Translate business questions into AI‑assisted analytical approaches, including natural‑language querying, statistical analysis, model interpretation, pattern detection, and predictive or other analytical modeling.
Prototype and test AI‑enabled analytical methods that augment existing reporting, dashboards, and analysis workflows.
Develop low‑code or no‑code automations within existing software that streamline routine data preparation, analysis, and reporting tasks.
Apply and interpret statistical, mathematical, or predictive models, as appropriate, to support operational planning, evaluation, forecasting, and decision-making.
Collaborate with data engineers or platform teams to operationalize repeatable analytical processes.
Validate AI outputs for accuracy, bias, and relevance before use in decision‑making.
Integrate AI-supported reporting, trend detection, anomaly identification, and analytical modeling to support dashboards, reports, and ad‑hoc analyses.

Communication and Stewardship of AI Solutions

Collaborate with stakeholders to explain AI‑supported insights, analytical findings, and modeling results in clear, non‑technical language.
Serve as a resource for responsible AI use, helping analysts understand appropriate use cases and limitations.
Follow organizational standards for data governance, privacy, and responsible AI use.
Document AI‑assisted workflows, analytical methodologies, modeling assumptions, and sources to ensure transparency and reproducibility.
Stay informed on emerging AI tools and techniques relevant to data analysis and modeling, recommending adoption when value‑aligned.
Supports technical projects as part of a project team.
Evaluate internal systems for efficiency, problems, and inaccuracies.
Performs related work as required.

Minimum Qualifications

Bachelor's degree in information technology, business administration, data analysis or related field, and six years of related experience.
Experience applying AI tools to improve real-world workflows (e.g., analysis, drafting, summarization, customer support, knowledge search, or automation).
Experience performing statistical data analysis and modeling using large datasets, with practical experience in understanding their use in making informed decisions.
Experience evaluating and operating AI solutions (test cases/metrics, monitoring/logging) with a strong grounding in responsible AI (privacy, security, bias, accessibility).
Experience taking an AI-enabled workflow from prototype to pilot/production, including stakeholder feedback, documentation, and basic automation/API integration.
Ability to design, iterate, and document prompts/workflows; validate AI outputs for accuracy, bias, and appropriateness before use.
Ability to translate stakeholder needs into clear problem statements, success criteria, and practical AI-enabled solutions.
Working knowledge of AI/ML and generative AI fundamentals, including common limitations and risks (e.g., model evaluation basics, hallucinations, and prompt sensitivity).
Proficiency with at least one scripting or automation approach (e.g., Python, SQL, PowerShell, R, or a low-code platform) to streamline and standardize processes.
Knowledge of responsible AI, privacy, and security practices, including appropriate handling of sensitive data when using AI systems.
Knowledge of statistical models, data analysis techniques, predictive modeling, and/or optimization.
Experience communicating analytical findings to non-technical stakeholders.

Preferred Qualifications

Hands-on experience building AI-assisted solutions with large language models (LLMs), including prompt/workflow design and structured outputs.
Familiarity with retrieval-augmented generation (RAG), semantic search, embeddings, and working with unstructured content (documents/knowledge bases).
Experience applying advanced analytical, statistical, predictive, or mathematical modeling techniques to evaluate operational performance, identify trends and patterns, and develop data-driven recommendations.

Appointment Terms

Terms and Conditions of Employment

Employment of the successful candidate is contingent upon the successful completion of a pre-employment criminal background check.

All employees are subject to adherence to the State Code of Ethics.

The University of Connecticut is an AA/EEO employer including for Disability and Veteran status.

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IT-SLE Operations Analyst

Edtech.com's Summary

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