Data Scientist II

Universities of Wisconsin•Madison, WI

22h•Remote

About The Position

The Office of Informatics is seeking an outstanding candidate for a Data Scientist II position to support research related data needs. The Data Scientist II will design, develop, and maintain data pipelines, data models, visualizations and analytical tools to support research and decision-making across multiple stakeholder groups. This role works collaboratively with researchers, analysts, and other partners to transform complex data into accessible, actionable insights, including geospatial data and visualizations. The Data Scientist II will leverage SQL, Python, dashboarding and GIS tools to build scalable data solutions, create engaging visualizations and maps, and enable efficient data access for research use cases.

Requirements

Professional experience developing Power BI, Tableau, or similar dashboards for multiple stakeholders
Experience writing complex SQL queries for data extraction and transformation
Experience using Python for data processing, scripting, or automation
Demonstrated ability to translate business needs into data visualizations
Strong analytical, problem-solving, and communication skills

Nice To Haves

Experience with data modeling concepts (star schema, dimensional modeling)
Familiarity with data warehousing platforms (e.g., Snowflake, Redshift, Azure Synapse)
Experience with Power BI Service, including publishing and governance
Knowledge of data governance, security, and best practices
Experience working in a collaborative or matrixed environment
Experience working with geographic information systems (GIS) and spatial data
Ability to design and create engaging, user-friendly maps and geospatial visualizations for research and operational use cases

Responsibilities

Develops and Maintains Data Solutions: Design and build interactive visualizations for a variety of stakeholder groups. Develop and maintain scalable data pipelines and data models to support research and operations. Incorporate geospatial data into dashboards and analytical workflows where appropriate. Ensure solutions follow data visualization best practices and accessibility guidelines.
Data Integration and Transformation: Develop and maintain data pipelines using SQL and Python. Clean, transform, and validate datasets from multiple sources, including spatial datasets. Create reusable data models and semantic layers within Power BI. Support integration of GIS data sources into enterprise data environments.
Stakeholder Collaboration and Requirements Gathering: Translate requirements into technical solutions, including dashboards and geospatial visualizations. Partner with stakeholders to understand research data needs, including location-based analysis. Communicate insights effectively to both technical and non-technical audiences.
Documentation and Governance: Document data sources, workflows, dashboards, and spatial data assets. Support data governance, quality, and consistency standards. Maintain version control and standard deployment processes.
Continuous Improvement and Support: Monitor and troubleshoot data pipelines, dashboards, and geospatial outputs. Stay current with Azure, Power BI, GIS tools, and data engineering best practices. Recommend enhancements to improve data accessibility, visualization, and spatial analysis capabilities.
Prepares data sets for analysis including cleaning/quality assurance, transformations, restructuring, and integration of multiple data sources.
Composes and assembles reproducible workflows and reports to clearly articulate patterns to researchers and/or administrators.
Organizes and automates project steps for data preparation and analysis.
Independently identifies and implements appropriate data science techniques to find data patterns and answer research questions chosen by the lead researcher including data visualization, statistical analysis, machine learning, and data mining.
Documents approaches to address research questions and contributes to the establishment of reproducible research methodologies and analysis workflows.