The University of Rochester is seeking to improve its data-driven decision-making capabilities by hiring a professional to provide specialized depth and breadth of expertise in the design, implementation, and dissemination of advanced analytics and data science projects.
Requirements
- Considerable experience with relational databases required
- Proficiency with Python (or R), including common machine/deep learning libraries and their implementation required
- Fluency with data visualization tools, such as Tableau, Spotfire or PowerBI required
- Strong analytic, qualitative and quantitative data analysis skills, with demonstrated ability to break down complex problems into phased deliverables required
- Ability to work independently and successfully complete assigned tasks on time without direct oversight required
- Strong attention to detail, ability to develop creative solutions to complex problems, and strong critical thinking skills required
- Experience with machine learning and related analytic approaches
Responsibilities
- Translates complex data into insights and compelling stories.
- Presents data models, analyses and visuals, including dynamic visuals such as dashboards, to internal and external stakeholders.
- Analyzes and models structured data and implements algorithms to support analysis using advanced statistical and mathematical methods.
- Designs, develops and implements robust, reproducible processes to prepare, extract and enrich a variety of structured and unstructured data sources.
- Creates and implements data science solutions to support analytical efforts and expand dashboard capabilities.
- Completes agile development of data models, ML pipelines, dashboards and reporting.
- Cultivates new data sources to offer insights and analyses needed for research and decision-making.
Other
- Bachelor's degree and 4 years of relevant experience required
- Master's degree preferred
- Or equivalent combination of education and experience
- Excellent verbal and written communication and presentation skills, including the ability to describe data science concepts to lay/nonexpert audiences required
- Ability to work independently and as part of a team
