About the position
Mayo Clinic is seeking a Principal Data Science Analyst to join our team in a fully remote capacity. This role is pivotal in performing detailed analysis of large bodies of heterogeneous data to discover new patterns and insights that significantly impact patient health and augment human capabilities. The ideal candidate will possess deep expertise in artificial intelligence (AI), machine learning, deep learning, statistical data processing, regression techniques, neural networks, decision trees, clustering, pattern recognition, and probability theory. A strong understanding of healthcare data types, topics, and the scientific challenges associated with them is essential. In this position, you will collaborate with knowledge architects, informaticians, and clinicians at Mayo Clinic, as well as partner with external companies to develop and deploy applications that bring AI and analytic solutions to non-technical users, often at the point of care. You will be responsible for designing and developing scripts or software applications to support data management, extraction, analysis, and AI as required. This role may also involve developing predictive and prescriptive models to address complex problems, discover insights, and identify opportunities using machine learning, statistical techniques, and data mining. As a Principal Data Science Analyst, you will provide consultative services at an enterprise level to various departments and divisions, potentially leading scientific projects. You will be expected to provide deep data insights for complex business problems, develop predictive and prescriptive models, and make presentations on assigned projects or proposals. Your responsibilities will also include conducting advanced data analysis, designing highly complex algorithm systems, and leading the interpretation of data analysis while writing comprehensive reports. The role requires a high degree of independence and judgment in handling delegated responsibilities, as well as experience in leading technical and quantitative teams.
Responsibilities
- Perform detailed analysis of large bodies of heterogeneous data to discover new patterns and insights impacting patient health.
- Collaborate with knowledge architects, informaticians, and clinicians to develop and deploy AI and analytic solutions.
- Design and develop scripts or software applications for data management, extraction, analysis, and AI.
- Develop predictive and prescriptive models to address complex problems using machine learning and statistical techniques.
- Provide consultative services at an enterprise level to departments and divisions.
- Lead scientific projects and provide deep data insights for complex business problems.
- Conduct advanced data analysis and design highly complex algorithm systems.
- Make presentations on assigned projects or proposals.
- Develop experimental design approaches to validate findings or test hypotheses.
- Lead and direct the interpretation of data analysis and writing reports.
Requirements
- A Master's degree in a relevant field such as engineering, mathematics, computer science, health science, or other analytical/quantitative fields.
- A minimum of five years of professional or research experience in data science.
- Preferred: A PhD in a relevant field and a minimum of three years of professional or research experience in data science and statistical/machine learning packages.
- Strong leadership skills and a technical and business background/experience.
- Excellent written and oral communication skills.
- Deep expertise in scientific computing and data management packages.
- Ability to prioritize, organize, and delegate tasks on projects.
- Demonstrated success in project management and communication skills.
- Experience developing predictive and prescriptive models on large-scale datasets.
Nice-to-haves
- Experience providing consultative services at an institutional or enterprise level.
- Demonstrated application of problem-solving methodologies and planning techniques.
- Experience with data modeling and data exploration tools.
Benefits
- Continuing education credits
- Opportunities for advancement
- Comprehensive benefit plans
- Competitive compensation
