Senior Data Science Analyst - AI Program

Mayo Clinic•Rochester, MN

2d•$127,962 - $185,515

About The Position

Mayo Clinic is seeking a Senior Data Science Analyst for their AI Program. This role involves performing 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 that impact patient health and augment human capabilities. The candidate will leverage deep expertise in AI, machine learning, deep learning, multimodal model development, statistical data processing, and various mathematical theories underlying data analysis tools. A strong understanding of healthcare data types, modalities, topics, and scientific challenges, including the safe application of large language models (LLMs) and multimodal foundation models in healthcare, is essential. The role may involve collaboration with internal knowledge architects, informaticians, and clinicians, as well as external partners, to develop and deploy AI solutions for non-technical users, often at the point of care. Responsibilities include applying and modifying scripts/software for data management, extraction, analysis, and AI, as well as developing predictive models on large-scale datasets using advanced statistical modeling, operations research, machine learning, or data mining techniques. The position may also involve providing enterprise-level consultative services and supporting scientific projects under senior supervision.

Requirements

A Bachelor’s degree in a relevant field such as engineering, mathematics, computer science, health science and at least 12 graduate level semester hours in domain-relevant sciences or seven years or more of data science and AI experience.
Technical and business background/experience along with strong leadership skills.
Good written and oral communication skills.
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 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.
Incumbent must have ability to manage a varied workload of projects with multiple priorities and stay current on healthcare trends and enterprise changes.
Strong interpersonal skills to include presentation, negotiation, persuasion, and written communications skills.
Strong time management skills.
Strong 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

PhD or Master’s degree with one year experience in a relevant field such as engineering, mathematics, computer science, health science, or other analytical/quantitative field and a minimum of one year of professional or research experience in data.
Demonstrated ability to provide consultative services to departments/divisions and committees from any Mayo entity requesting assistance.
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.
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.
Strong 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.
Experience participating in cross functional 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

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.
Develop and deploy applications to bring AI, foundation model and analytic solutions to nontechnical users, often at the point of care.
Apply and modify existing scripts or software applications to support data management, data extraction, data analysis, and AI as required.
Develop predictive models on large-scale datasets to address various business problems through leveraging advanced statistical modeling, operations research, machine learning, or data mining techniques.
Provide Consultative Services at an enterprise level to departments/divisions and/or support scientific projects under the supervision of a designated senior level data scientist.
Apply analytics techniques to extract insights from structured, unstructured, and multimodal healthcare data, including text, imaging, genomics, and other clinical data types.
Develop 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.
Work with other staff in developing data analysis tools and predictive models, using advanced data analysis techniques and artificial intelligence and machine learning.
Participate in discovery processes with stakeholders to identify the business requirements and the expected outcome.
Support the use and adaptation of algorithms and models, including machine learning, LLM, and multimodal approaches, to identify patterns across healthcare data modalities.
Apply working knowledge of LLMs and foundation models to support prompting, evaluation, and safe use in healthcare applications.
Lead the interpretation of data analysis and writing reports.