Service Data Scientist

GE HealthCare•Salt Lake City, UT

21d

About The Position

The Sr Data Scientist will work in teams addressing statistical, machine learning and data understanding problems in a commercial technology and consultancy development environment. 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.

Requirements

Bachelor's Degree in Computer Science or “STEM” Majors (Science, Technology, Engineering and Math) with advanced experience.
Demonstrated skill in the use of one or more analytic software tools or languages (e.g., SAS, SPSS, R, Python)
Demonstrated skill at data cleansing, data quality assessment, and using analytics for data assessment
Demonstrated skill in the use of applied analytics, descriptive statistics, feature extraction and predictive analytics on industrial datasets
Demonstrated skill at data visualization and storytelling for an audience of stakeholders
Demonstrated awareness of data management methods
Demonstrated awareness of real-time analytics development and deployment
Demonstrated awareness of industry and technology trends in one or more sectors where GE operates
Demonstrated awareness of customer and stakeholder management and business metrics
Demonstrated skill at function in a team setting
Demonstrated awareness of critical thinking and problem solving methods
Demonstrated awareness of presentation and influencing skills
Demonstrated awareness of how to succeed in ambiguous circumstances

Responsibilities

Develop analytics to address customer needs and opportunities.
Work alongside software developers and software engineers to translate algorithms into commercially viable products and services.
Work in technical teams in development, deployment, and application of applied analytics, predictive analytics, and prescriptive analytics.
Perform exploratory and targeted data analyses using descriptive statistics and other methods.
Work with data engineers on data quality assessment, data cleansing and data analytics
Generate reports, annotated code, and other projects artifacts to document, archive, and communicate your work and outcomes.
Share and discuss findings with team members.