AI Algorithm Developer
Senior data scientist role (posted as AI Algorithm Developer) with GE HealthCare, building and deploying machine learning, statistical, and operational-research models on large commercial/industrial datasets. Day to day: exploratory data analysis, predictive and prescriptive analytics, and working with engineers to turn algorithms into products, using tools like Python, R, SAS, or SPSS.
- Compensation
- $109.6K–$164.4K / yr
- Location
- Not listed
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Contract
- Experience
- Not specified
- Category
- Not specified
- Posted
Skills
Must have
- Bachelor's Degree in Computer Science or STEM major with advanced experience (roles outside USA)
- Bachelor's Degree in Computer Science or STEM major with minimum 4 years experience (USA roles)
Nice to have
- Skill in one or more analytic software tools or languages (SAS, SPSS, R, Python)
- Skill at data cleansing, data quality assessment, and using analytics for data assessment
- Skill in applied analytics, descriptive statistics, feature extraction and predictive analytics on industrial datasets
- Skill at data visualization and storytelling for stakeholders
- Awareness of data management methods
- Awareness of realtime analytics development and deployment
- Awareness of industry and technology trends in sectors where GE operates
- Awareness of customer and stakeholder management and business metrics
- Ability to function in a team setting
- Awareness of critical thinking and problem solving methods
- Presentation and influencing skills
- Ability to succeed in ambiguous circumstances
Full description
Job Title: AI Algorithm Developer
Job Location: Remote
Job Location Type: Remote
Job Contract Type: Full-time
Job Seniority Level:
Job Description Summary
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.
Job DescriptionRoles and Responsibilities
As a Sr Data Scientist, you will be part of a data science or cross-disciplinary team on commercially-facing development projects, typically involving large, complex data sets. These teams typically include statisticians, computer scientists, software developers, engineers, product managers, and end users, working in concert with partners in GE business units. Potential application areas include remote monitoring and diagnostics across infrastructure and industrial sectors, financial portfolio risk assessment, and operations optimization.
In this role, you will:
- 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.
Education Qualification
For roles outside USA:
Bachelor's Degree in Computer Science or “STEM” Majors (Science, Technology, Engineering and Math) with advanced experience.For roles in USA:Bachelor's Degree in Computer Science or “STEM” Majors (Science, Technology, Engineering and Math) with minimum years of experience4years
Desired Characteristics• 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