paid / Samford, CT (remote) / full-time
Are you an R / RStudio champion? Have you been involved in designing, installing, configuring, and deploying RStudio Pro at an enterprise level? Do you believe in supporting self-service solutions?
Join our team!
The Data Science Engineer is a member of the Enterprise Data and Analytics (EDA) team within the Global IT Services. In this capacity, the candidate will work with teams of passionate and skilled data engineers, architects, and analysts responsible for building batch and streaming data pipelines and support all the analytics, business intelligence, data science needs across the organization.
The candidate must be highly collaborative, organized, and analytical, and is expected to partner effectively with other teams within IT, business, and service units on an ongoing basis.
Data Science Engineer Responsibilities:
- Engage in the evolution of data analytics based on the strategic objective of being a data-oriented and analytics-driven company.
- Collaborate with business functions on defining business cases for data and analytical projects, based on an understanding of value drivers and knowledge of technical possibilities.
- Initially, the focus will be on the maintenance and refinement of the recently deployed RStudio servers across multiple regions, which will later expand to include Azure cloud-based services such as Azure Data Factory, Azure Databricks, and Power BI.
- Collaborations with both the business and IT teams to define business problems, refine the requirements, and design and develop analytics systems and/or end-to-end solutions.
- As an analytics system champion, engagement with stakeholders to communicate the vision for analytics, understand and address pain points, diplomatically overcome resistance to adoption, and demonstrate the value.
- Network with key business stakeholders to be an advocate and ambassador for the analytics systems.
- Develop and maintain statistical programming standards and best practices to be advocated by IT and used by business.
- Regularly meet and discuss with analytics system consumers on refining and enhancing in nonproduction environments and deploying them in production.
- Prepare data analytics solution proposals for business cases based on up-to-date knowledge of technical possibilities and overall data and analytics strategies.
- Ability to translate data-oriented proofs-of-concept, and end user analytical models, into enterprise scale data analytics applications.
- Develop organizational knowledge of key data sources and be a valuable resource to people in the company on how to best use data analytics to pursue company objectives.
- Promote the use of DevOps and MLOps in the delivery of solutions by internal and external development teams.
- Keep up to date on the industry best practices in data and advanced analytics.
GITS Data Science Engineer.pdf (940.2 KB)