Data Scientist

General Electric

Qualifications/Requirements
•Bachelor’s Degree in a “STEM” major (Science, Technology, Engineering, Mathematics)
•Minimum 1 year analytics development in a commercial setting
•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, and predictive analytics
on industrial datasets

Desired Characteristics
Technical Expertise:
•Demonstrated awareness of feature extraction and real-time analytics methods
•Demonstrated awareness of analytic prototyping, analytic scaleup, analytic scaling, and solutions
integration

Domain Knowledge:
•Demonstrated awareness of industry and technology trends in data science
•Demonstrated awareness of customer and stakeholder management and business metrics

Leadership:
•Demonstrated awareness of how to function in a team setting
•Demonstrated awareness of critical thinking and problem solving methods
•Demonstrated awareness of presentation skills

Personal Attributes:
•Demonstrated awareness of how to leverage curiosity and creativity to drive business impact

İŞ TANIMI

Role Summary/Purpose
The 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.
Essential Responsibilities
In this role, you will:
•Develop analytics within well defined projects 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.