Principal Data Science Analyst

Data Scientists at Mayo Clinic perform detailed analysis of large bodies of heterogeneous data in order to discover new patterns and insights having an impact upon patient health and augmenting human capabilities. Candidate has deep expertise in AI, machine learning, deep learning, statistical data processing, regression techniques, neural networks, decision trees, clustering, pattern recognition, probability theory and data science methods and the mathematical theories underlying these tools used to analyze data. Has deep knowledge of healthcare data types, topics, and scientific challenges and approaches.

Work with knowledge architects, informaticians and clinicians at Mayo, and partner outside companies to develop and deploy applications to bring AI and analytic solutions to nontechnical users, often at the point of care.. Designs and develops scripts or software applications to support data management, data extraction, data analysis, and AI as required. This position may develop predictive and prescriptive models to address complex problems, discover insights, and identify opportunities using machine learning, statistical techniques, and data mining. Provides Consultative Services at an enterprise level to departments / divisions and / or may lead scientific projects. May have direct and indirect reports.

Other responsibilities :

• Provides deep data insights for complex business problems that can be approached with analytics techniques to collect, explore, and extract insights from structured and unstructured data.
• Develops predictive and prescriptive models to address complex problems, discover insights, and identify opportunities using machine learning, statistical techniques, and data mining.
• Makes presentations on assigned projects or proposals.
• Conducts advanced data analysis and designs highly complex algorithm systems.
• Functions independently and initiates judgment in handling delegated responsibilities.
• Experience leading technical / quantitative teams.
• Develops experimental design approaches to validate findings or test hypotheses.
• Identifies / creates the appropriate algorithm to discover patterns.
• Leads and directs the interpretation of data analysis and writing reports.