The Well-Being Service Area of Human Services seeks a Data Analyst focused on data analysis for the Youth Education Team. This position will provide data collection, tracking and analytics support to the Connect for School Success, the Education Support Services Program in the ESS Department, and the Education Domain of Disparity Reduction. In addition, this position will also create and maintain program dashboards in Power BI for these education programs with data visualizations that show the impact of different programs and interventions on residents and guide managers in making necessary changes to improve program outcomes and make programmatic decisions to expand services that are having the most impact.
Location and hours:
This position is remote. Remote workers may be required to come on-site an average of 0 to 12 days a year for work meetings, trainings, or for any other reasons that their supervisor may deem necessary (may include additional days for onboarding and new employee training). Supervisors will provide as much advance notice as practicable. Work hours are flexible, Monday through Friday, between 7:30 a.m. and 5 p.m. Based on business needs of this position, hires must live in or relocate to Minnesota or Wisconsin.
In this position, you will:
Query, transform, and clean data from a variety of sources.
Analyze data and identify trends, patterns, and insights.
Visualize data in engaging and informative ways for various stakeholders.
Develop reports and dashboards from concept to production.
Participate in continuous improvement activities related to data practices and workflows.
Identify and understand the needs of different stakeholders, including general staff, leadership, and data coordinators.
Collaborate with business partners to define business needs, articulate business and research questions, and find relevant data sources.
Develop actionable insights from data analysis that can be used to improve the lives of county residents in various ways.
Communicate actionable insights to stakeholders and decision-makers clearly and concisely.
Need to have:
One of the following:
Bachelor's degree or higher in any field and a minimum of two courses in research methods, analysis, statistics, project management, planning or evaluation and four years or more of relevant planning analyst and/or project management experience.
Bachelor's degree or higher in data analytics, business analytics, business administration, public administration, research/evaluation methods, statistics, behavioral/social science, public health, computer science/MIS, management science, urban/city, or a related fieldand three years or more of relevant planning analyst and/or project management experience.
Master's degree or higher in one of the fields listed above and two years or more of relevant planning analyst and/or project management experience.
Ph.D. degree in one of the fields listed above.
Nice to have:
Collecting, analyzing, and interpreting data, with attention to detail and accuracy, to help improve business/organizational practices.
Telling stories through data visualization using user-centered design best practices.
Querying and transforming data from large and complex databases using SQL or similar tools.
Report development using business intelligence or reporting tools (Power BI, SSRS, QlikView, Tableau, etc.).
Identify and solve problems using data and apply analytical and creative thinking to make data-informed recommendations for action.
Think critically about data, identify potential biases and errors in data, and evaluate the validity of data sources.
Work independently and successfully manage multiple priorities.
Build and nurture collaborative relationships with a variety of business partners.
Clearly and concisely explain complex data in a way that is easy to understand, verbally and in writing.
Utilize available resources to continually learn and develop professionally.
Computer systems used in these areas (MAXIS, SSIS, or similar systems).
Statistics and experience using statistical packages for analyzing datasets (R, Python, SAS, Stata, etc.).
Data science process and principles (i.e., business/data understanding, data preparation, modeling - statistics/machine learning, evaluation, and deployment).