Job Description Summary

As a Data Analyst Specialist, you will support AI and analytics initiatives by performing exploratory data analysis (EDA), statistical analysis, and data wrangling—the foundational work required for model building and feature engineering. You’ll work closely with Data Scientists and AI Engineers to prepare high-quality datasets, uncover insights, validate assumptions, and help improve data readiness for machine learning and advanced AI use cases. You will work in teams addressing statistical, machine learning and data understanding problems. In this role, you will contribute to the development and deployment of modern machine learning, operational research, semantic analysis, and statistical methods for finding structure in large data sets.

This is an ideal role for someone who enjoys working hands-on with data, learning best practices from senior technical partners, and building strong analytical fundamentals in a real-world AI environment.

GE HealthCare is a leading global medical technology and digital solutions innovator. Our mission is to improve lives in the moments that matter. Unlock your ambition, turn ideas into world-changing realities, and join an organization where every voice makes a difference, and every difference builds a healthier world.

Job Description

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 (handling missing values, duplicates, outliers, inconsistent formats).

• Build and maintain analysis-ready datasets to support feature engineering and model development.

• Apply data quality checks (e.g., row counts, referential integrity, distribution checks) and document findings

Exploratory Data Analysis (EDA)

• Perform EDA to understand data structure, relationships, distributions, and anomalies.

• Identify trends, patterns, and data issues that may impact modeling performance 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 (e.g., correlations, variance comparisons, hypothesis tests where appropriate).

• Assist in measurement design, KPI definitions, and experimental analysis support (as needed).

• Help validate model inputs, features, and labels by analyzing data consistency and potential leakage risks.

Collaboration & Documentation

• Work in technical teams in development, deployment, and application of applied analytics, predictive analytics, prescriptive analytics and GenAI applications.

• Maintain well-structured documentation for datasets, assumptions, and analysis steps.

• Partner with Data Scientists and AI Engineers to translate requirements into data deliverables.

• Contribute to reproducible analysis using established data practices, code review practices, and version control workflows.

• Work with data engineers on data quality assessment, data cleansing and data analytics

Data Governance & Responsible Use

• Follow established data governance, privacy, and security policies.

• Handle sensitive data responsibly and ensure proper access controls and documentation are in place.

• Generate reports, annotated code, and other projects artifacts to document, archive, and communicate your work and outcomes.

• Share and discuss findings with team members.

Requirements

• Bachelor’s degree (or equivalent practical experience) in a quantitative field such as Statistics, Mathematics, Economics, Computer Science, Data Science, Engineering, or similar with 0-3 years of working experience.

• Familiarity with SQL for querying and manipulating data (joins, aggregations, filters).

• Working knowledge of Python for data analysis (e.g., pandas/tidyverse, basic scripting).

• Understanding of foundational statistics (distributions, summary stats, correlation, basic hypothesis testing concepts).

• Ability to communicate clearly—especially when summarizing insights, assumptions, and data limitations.

• Strong attention to detail and comfort working with messy or incomplete datasets.

• Experience with data visualization tools (e.g., Tableau, Power BI) and/or Python visualization libraries.

Desired Characteristics

• Exposure to modern data environm