About Us: GE is the world's Digital Industrial Company, transforming industry with software-defined machines and solutions that are connected, responsive and predictive. Through our people, leadership development, services, technology and scale, GE delivers better outcomes for global customers by speaking the language of industry. GE offers a great work environment, professional development, challenging careers, and competitive compensation. GE is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.
Role Summary: The Data Scientist will partner with researchers and Digital Technology team members to design and implement solutions using the latest Machine Learning technologies and methodologies. Help transition the most impactful solutions into new services for the Company in partnership with other team members. Focus will be on leveraging analytics and Machine Learning techniques to optimize and automate workflows; mine data for new insights; implement related techniques in new services and products.
Essential Responsibilities: The Data Scientist will be part of a cross-disciplinary team working on research and commercially-facing development projects, typically involving large, complex data sets. These teams typically include software developers, DevOps engineers, product managers, computer scientists, and end users, working in concert with partners in GE business units.
Recent examples of solutions include implementing the infrastructure and foundational software ecosystem supporting the Digital Twin development platform; operationalizing containerization technologies like Docker and Kubernetes to support standardized deployments of machine learning frameworks; and deploying Machine Learning and Deep Learning frameworks on DGX-1 systems as well as on Azure and AWS to accelerate key research projects.
Potential projects include assisting with the creation of a Machine Learning service offering for the Company, integrating neural networks to optimize simulation workflows in the Company’s High Performance Computing service, etc. 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 on data quality assessment, data cleansing and data analytics. Generate reports, annotated code, and other projects artifacts to document, archive, and communicate the work and outcomes. Share and discuss findings with team members.
Bachelor’s Degree in a “STEM” major (Science, Technology, Engineering, Mathematics)
Legal authorization to work in the U.S. is required. GE may agree to sponsor an individual for an employment visa now or in the future if there is a shortage of individuals with particular skills.
Computer Science Degree
Experience managing IT infrastructure and software with strong desire to grow AI skillsets; or experience developing AI solutions with a strong desire to grow IT infrastructure and software implementation skills.
Experience with parallelization of software and/or algorithms
Minimum 1 year analytics development in a commercial setting
Demonstrated awareness of feature extraction and real-time analytics methods
Demonstrated awareness of analytic prototyping, analytic scaleup, analytic scaling, and solutions integration
Demonstrated skill in the use of one or more analytic software tools or languages (e.g., SAS, SPSS, R, Python)
Demonstrated skill in the use of one or more ML/DL frameworks (e.g. TensorFlow, Caffe2, Theano, Torch, CTPN, MXNet)
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
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