Data Analyst & GenAI Specialist, Enterprise AI

GE HealthCare•Chicago, IL

4d•Onsite

About The Position

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.

Requirements

Bachelor’s degree (or equivalent practical experience) in a quantitative or technical field such as Statistics, Mathematics, Economics, Computer Science, Data Science, Engineering, Information Systems, or similar, with 0-3 years of relevant work experience.
Familiarity with SQL for querying and manipulating data, including joins, aggregations, and filters.
Working knowledge of Python for data analysis, such as pandas/tidyverse and basic scripting.
Understanding of foundational statistics including distributions, summary statistics, correlation, and basic hypothesis testing concepts.
Ability to communicate clearly, especially when summarizing insights, assumptions, limitations, and process improvement opportunities.
Strong attention to detail and comfort working with messy, incomplete, or evolving datasets and business requirements.
Experience with data visualization tools such as Tableau or Power BI and/or Python visualization libraries.
Demonstrated interest in applying generative AI tools to business workflows and process improvement.

Nice To Haves

Hands-on familiarity with one or more enterprise GenAI platforms or adjacent workflow tools is preferred, including Microsoft 365 Copilot, Copilot Studio, Power Automate, ChatGPT Enterprise, custom GPTs, Claude, or similar solutions.
Exposure to cloud platforms such as AWS or Azure is a plus.
Familiarity with ML/AI concepts including features, labels, training versus inference data, and evaluation metrics is preferred.
Experience using Git and writing reproducible notebooks, documentation, or workflow playbooks is a plus.

Responsibilities

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.
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.
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.
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.
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.
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.