The Select Group is looking for a Data Scientist to join the team at one of their utility clients! This person will build data products and solutions to complex and advanced data problems faced throughout the organization. They will also partner with various business clients, data engineers, and data analysts to ensure your stakeholders have the advanced analytics support they need. Areas of focus include classification, regression, natural language processing, forecasting, change point detection, sentiment analytics, etc. The ideal candidate will have experience collaborating with business SMEs and communicate results to a non technical audience.
Requirements:
Minimum of (5) years of experience working as a data scientist or in a related field
Experience with ArcGIS Pro toolkits (i.e. Spatial Analyst, Spatial Statistics, Network Analyst)
5+ years experience with ArcGIS or other geospatial tools
Experience with SQL, and relational or schema-less (NoSQL) databases
Experience with programming languages: Python, R, MATLAB, etc.
Experience with data visualization tools: Tableau, PowerBI, Looker, etc.
Experience with cloud technologies: Google Cloud Platform, AWS, Azure, or similar technologies
Bachelors Degree in quantitative discipline: Statistics, Mathematics, Economics, Information Systems, or related field
Responsibilities:
Apply advanced analytics methods to address business problems.
Develop, tune, and deploy statistics & machine learning models including but not limited to classification, regression, natural language processing, forecasting, change point detection, sentiment analytics, etc.
Use version control and GIT principles to collaborate and maintain a codebase alongside peers.
Use basic MLOps principles to deploy and monitor models in production environments.
Work within Google Cloud Platform (GCP) to design and manage components of a data product or service.
Perform exploratory data analysis that supports or justifies hypothesis about data-related questions.
Partner with business units to define data problems, measurable success criteria, and actionable deliverables.
Leverage data visualization tools to create compelling stories about insights you find and discover.
Design and build experiments to measure the success from the models in production.
Communicate insights, updates, and outcomes in a way that is understood by non-technical stakeholders.
Use data engineering techniques to identify required data sources, wrangling data, engineering new features, and related techniques.
Collaborate with cross-functional teams to identify opportunities for process optimization, cost reduction, and revenue generation using data-driven insights.