Job Description SummaryAs 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.