Principal Data Science Analyst

Mayo Clinic•Rochester, MN

About The Position

Data Scientists at Mayo Clinic perform detailed analysis of large bodies of heterogeneous healthcare data, including imaging, genomics, clinical text, structured EHR data, signals, and other healthcare modalities 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, multimodal model development, 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, modalities, topics, and scientific challenges, including how large language models and multimodal foundation models are built, adapted, evaluated, and safely applied in healthcare settings. Work with knowledge architects, informaticians and clinicians at Mayo, and partner outside companies to develop and deploy applications to bring AI, foundation model 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: Applies analytics techniques to extract insights from structured, unstructured, and multimodal healthcare data, including text, imaging, genomics, and other clinical data types. Develops predictive, prescriptive, generative, and multimodal models to address complex healthcare problems, discover insights, and identify opportunities using machine learning, statistical techniques, data mining, and foundation model approaches. 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, or adapts appropriate algorithms and models, including LLMs and multimodal foundation models, to discover patterns across healthcare data modalities. Applies knowledge of how LLMs and foundation models are trained, fine-tuned, prompted, evaluated, and governed to support safe and effective use in healthcare applications. Leads and directs 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 and a minimum of five years of professional or research experience in data science
Deep expertise in AI, machine learning, deep learning, multimodal model development, 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.
Deep knowledge of healthcare data types, modalities, topics, and scientific challenges, including how large language models and multimodal foundation models are built, adapted, evaluated, and safely applied in healthcare settings.
Experience leading technical/quantitative teams.
Demonstrated ability to develop predictive and prescriptive models on large-scale datasets to address various business problems through leveraging advanced statistical modeling, machine learning, or data mining techniques.
Demonstrated application of several problem-solving methodologies, planning techniques, continuous improvement methods, project management methods, and analytical tools and methodologies (e.g. machine learning, statistical packages, modeling, etc.) required.
Incumbent must have ability to independently manage a varied workload of projects with multiple priorities and stay current on healthcare trends and enterprise changes.
Excellent interpersonal skills to include presentation, negotiation, persuasion, and written communications skills are required.
Exceptional time management is required.
Excellent analytical skills, consulting skills, ability to identify and recommend solutions, advanced computer application skills and a commitment to customer service.
Experience with data modeling and date exploration tools.

Nice To Haves

A PhD in a relevant field such as engineering, mathematics, computer science, health science, or other analytical/quantitative field and a minimum of three years of professional or research experience in data science and statistical/machine learning packages.
Both technical and business background/experience along with strong leadership skills.
Good written and oral communication skills are required.
Deep expertise in the use of scientific computing and data management packages.
Ability to prioritize, organize, and delegate various tasks on projects.
Demonstrated initiative in administration, education (seminars, training), software development, and technical reports.
Demonstrated success in project management and communication skills.
Demonstrated ability to provide consultative services at an institutional or enterprise level to departments/divisions and committees from any Mayo entity requesting assistance is preferred.
Strong expertise in AI/ML techniques and frameworks, such as deep learning, natural language processing, computer vision, multimodal AI, representation learning, and Generative AI, with proficiency in tools like Python, TensorFlow, PyTorch, sci-kit-learn, Keras, etc.
Experience with healthcare industry informatics standards, best practices, and common data models.
Excellent communication, collaboration, and stakeholder management skills, with the ability to effectively engage with diverse stakeholders and translate complex technical concepts and results to non-technical audiences.
Demonstrated experience leading technical/quantitative teams in a regulated environment.
Strong problem-solving abilities, critical thinking skills, and a passion for driving innovation and positive change in healthcare through AI technology.

Responsibilities

Applies analytics techniques to extract insights from structured, unstructured, and multimodal healthcare data, including text, imaging, genomics, and other clinical data types.
Develops predictive, prescriptive, generative, and multimodal models to address complex healthcare problems, discover insights, and identify opportunities using machine learning, statistical techniques, data mining, and foundation model approaches.
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.
Develops experimental design approaches to validate findings or test hypotheses.
Identifies, creates, or adapts appropriate algorithms and models, including LLMs and multimodal foundation models, to discover patterns across healthcare data modalities.
Applies knowledge of how LLMs and foundation models are trained, fine-tuned, prompted, evaluated, and governed to support safe and effective use in healthcare applications.
Leads and directs the interpretation of data analysis and writing reports.
Designs and develops scripts or software applications to support data management, data extraction, data analysis, and AI as required.
Develops 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.