Senior Business Intelligence Manager

Mayo Clinic•Rochester, MN

49d•$112,000 - $157,900

About The Position

Mayo Clinic is top-ranked in more specialties than any other care provider according to U.S. News & World Report. As we work together to put the needs of the patient first, we are also dedicated to our employees, investing in competitive compensation and comprehensive benefit plans – to take care of you and your family, now and in the future. And with continuing education and advancement opportunities at every turn, you can build a long, successful career with Mayo Clinic.

Requirements

Master’s degree in Engineering Science, Management Information Systems, or related and 6 years of experience required.
Relational (SQL), Cloud Oriented Databases
Experience with Tableau and other visualization tools to create self-service tools and dashboards
Experience with data validation tests and QA testing
Collecting data from multiple sources
Integration of external data into existing ecosystem of prospect insights tools
Conducting statistical analysis using packing in R & Python programming language
Leveraging data science approaches including AI, machine learning, decision trees, clustering, and pattern recognition
Experience with various types of data (marketing data including digital, CRM and clinical data)

Responsibilities

Plays critical role supporting advancement of analytics and data science activities of Mayo Clinic.
Collaborates closely with Marketing team (including direct), Information Technology functional experts, and others as needed.
Acts as an internal consultant by applying high order analytics and business expertise to Mayo Clinic’s Departments.
Assists with creating business insights through world-class data science, analytical tools and approaches.
Identifies insights on prospects’ behaviors, financial performance, trends of giving, affinities and use of departmental resources.
Statistical modeling of behaviors, propensities and affinities, as well as sentiment for a topic or interest (leveraging AI, machine learning, decision trees, clustering, pattern recognition, and other data science approaches).
Self-service tools and dashboards supporting functional areas of department.
Integration of external data into existing ecosystem of prospect insights tools.
Decision support for functional areas of department.
Analysis supporting data-driven strategies and tactics development.
Input into data architecture supporting future and current needs.
Other ad hoc analysis as needed.