Data Analyst & GenAI Specialist, Enterprise AI

Job Description Summary

As a Data Analyst & GenAI Specialist, you will support AI and analytics initiatives through exploratory data analysis (EDA), statistical analysis, data wrangling, and practical application of low-code/no-code generative AI tools to improve IT business processes. You will work closely with Data Scientists, AI Engineers, business stakeholders, and process owners to prepare high-quality datasets, uncover insights, validate assumptions, and identify opportunities to automate, simplify, and accelerate work.

Job Description

As a Data Analyst & GenAI Specialist, you will support AI and analytics initiatives through exploratory data analysis (EDA), statistical analysis, data wrangling, and practical application of low-code/no-code generative AI tools to improve IT business processes. You will work closely with Data Scientists, AI Engineers, business stakeholders, and process owners to prepare high-quality datasets, uncover insights, validate assumptions, and identify opportunities to automate, simplify, and accelerate work.

This is an ideal role for someone who enjoys working hands-on with data, learning best practices from senior technical partners, and applying practical AI tools in real-world business settings to drive measurable efficiency and quality improvements.

Roles and Responsibilities

Data Wrangling & Dataset Preparation

• Extract, join, and transform data from multiple sources using SQL and/or data tools.
• Clean and preprocess structured and semi-structured data, including handling missing values, duplicates, outliers, and inconsistent formats.
• Build and maintain analysis-ready datasets to support feature engineering, model development, and business reporting needs.
• Apply data quality checks such as row counts, referential integrity checks, reconciliation steps, and distribution checks, and document findings.

Exploratory Data Analysis (EDA)

• Perform EDA to understand data structure, relationships, distributions, anomalies, and business context.
• Identify trends, patterns, and data issues that may impact modeling performance, reporting quality, or business interpretation.
• Create clear visualizations and summaries to communicate key insights to technical and non-technical stakeholders.

Statistical & Analytical Support

• Conduct descriptive and basic inferential statistical analyses, such as correlations, variance comparisons, and hypothesis tests where appropriate.
• Assist in measurement design, KPI definition, and experimental analysis support as needed.
• Help validate model inputs, features, and labels by analyzing data consistency, lineage, and potential leakage risks.

GenAI

• Use low-code/no-code GenAI tools to improve efficiency, speed, and quality in IT business processes.
• Design and implement practical GenAI-enabled solutions using enterprise tools including Microsoft 365 Copilot, Microsoft Copilot Studio, Power Automate with Copilot, ChatGPT Enterprise, custom GPTs, and Anthropic Claude.
• Create prompts, reusable workflows, templates, and lightweight AI assistants that help teams summarize content, draft communications, synthesize requirements, generate documentation, and automate repetitive knowledge tasks.
• Partner with process owners and functional stakeholders to identify high-value use cases for GenAI, evaluate feasibility, and translate needs into scalable low-code/no-code solutions.
• Test and refine GenAI outputs for accuracy, usefulness, tone, and business relevance, while documenting prompt patterns, guardrails, and usage guidance.
• Monitor adoption, user feedback, and business outcomes of GenAI solutions, and recommend enhancements based on performance and evolving needs.

Collaboration & Documentation

• Work in technical teams focused on the development, deployment, and application of applied analytics, predictive analytics, prescriptive analytics, and GenAI solutions.
• Maintain well-structured documentation for datasets, assumptions, analysis steps, prompts, workflow logic, and solution outputs.
• Partner with Data Scientists, AI Engineers, data engineers, and business stakeholders to translate requirements into data and AI deliverables.
• Contribute to reproducible analysis and solution development using established data practices, code review practices, and version control workflows.
• Generate reports, annotated code, process documentation, and other project artifacts to document, archive, and communicate your work and outcomes.
• Share findings, recommendations, and lessons learned with team members and stakeholders.

Data Governance & Responsible Use

• Follow established data governance, privacy, security, and responsible AI policies.
• Handle sensitive data responsibly and ensure proper access controls, documentation, and review practices are in place.
• Apply sound judgment when using GenAI tools, including validating outputs, protecting confidential information, and aligning usage with enterprise policies and approved toolsets.