Entry Level Healthcare Informatics Jobs in Minnesota

Understanding of the ways in which information (i.e. meaningful data) is converted into knowledge. Identifies substantive problems where different representations, algorithms, and methods can be used to inform practice. Leads the development, design, and translation of informatics solutions incorporating domain specific data, information and knowledge e.g. Clinical, HR or finance informatics solutions. Works with subject matter experts on designing the integration of informatics solutions into the clinical or business workflow. Evaluates new requests for derived information or knowledge, or knowledge delivery solutions, and works with informatics scientists, data scientists and knowledge engineers to identify the most relevant approaches, including translational informatics and data science approaches, to address a specific problem. Validates the solution for efficacy. Proficient in the informatics methods used to analyze data and advanced knowledge of healthcare data types, topics, approaches, and scientific challenges. May work with data scientists and software developers to build new tools to address gaps. Develops and improves software applications to support data management, extraction, and analysis as required. Designs analytical procedures in the framework of a specific project or group of interconnected projects. May develop and implement knowledge representations based on cognitive, social, or organizational theories suitable in the context of real problems, such as the analysis of genomic or clinical data in the electronic health record (EHR). Deep knowledge of data governance principles and best practices, including nomenclature, provenance/pedigree, and data structures. Applies best practices in cataloging and indexing of datasets and algorithms. Participates in development of policy for managing derived data. Other responsibilities:
Deep understanding of the workflows and information flows of clinical departments and how departmental solutions fit into the larger technical ecosystem. 
Triages new requests for derived information or knowledge and works with principal informatics scientists and/or data scientists to identify the most relevant existing approaches, adapting them to a specific problem. Validates the output for efficacy. May work with domain experts and/or data scientists to build new tools to meet clinical needs.
Works with clinical departments to ensure the adoption and application of newly derived knowledge into the practice.
Appreciation of clinical or business workflows and informatics/AI impact on them. 
Expertise in integrating newly derived knowledge into the clinical or business workflow.
Uses change management principles to devise change management plan.

A Bachelor's degree in a relevant field including but not limited to bioinformatics, clinical informatics, biomedical engineering, computer science, health science, or other analytical/quantitative and a minimum of three years of professional or research experience in translational informatics will be considered. The preferred candidate will possess a Master's degree or PhD in a relevant field such as bioinformatics, biomedical engineering, computer science, health science, or other analytical/quantitative field and a minimum of one year of professional or research experience in data. Appreciate and leverage the complex environment in implementing translational solutions, with respect to the socio-economical, behavioral, and ethical considerations. Core competencies in use the underlying technologies of networking, security, and cloud computing. Deep understanding of the technical ecosystem. Effectively leverages the data, identity, and integration platforms to translate health IT solutions into the practice. Strong understanding of digital strategies for connecting with internal and external systems such as integrating external clinical applications with the EHR. Draws upon the social, cognitive and behavioral sciences to inform the design and evaluation of informatics solutions. Proficient in human factors design principles and how to apply to clinical informatics.

Preferred qualifications:

• Experience working with AI-assisted workflows, including validation and troubleshooting of AI-generated cohort definitions, queries, or data extraction logic; interest in agentic AI technologies is preferred.
• Understanding of Mayo Clinic data infrastructure and experience collaborating with clinicians, informaticians, and data scientists to deliver research data solutions
• Experience and/or knowledge of clinical trial operations, virtual/decentralized clinical trials, clinical research data collection workflows, and experience with trial emulation, simulation, or synthetic controls.
• Experience writing and optimizing SQL queries for cohort identification, data extraction, and query validation.
• Experience with Python (or similar scripting language) for data manipulation, workflow automation, and data quality assessment.
• Experience working with enterprise EHR data, relational databases, and clinical data warehouses, including familiarity with standard clinical terminologies (e.g., ICD-10, CPT, LOINC, RxNorm, SNOMED CT).
• Experience translating clinical research questions and eligibility criteria into reproducible cohort definitions, data queries, and extraction logic.
• Knowledge of descriptive statistics and exploratory data analysis to support cohort characterization, data validation, and quality assurance. 

A Bachelor's degree in a relevant field including but not limited to bioinformatics, clinical informatics, biomedical engineering, computer science, health science, or other analytical/quantitative and a minimum of three years of professional or research experience in translational informatics will be considered. The preferred candidate will possess a Master's degree or PhD in a relevant field such as bioinformatics, biomedical engineering, computer science, health science, or other analytical/quantitative field and a minimum of one year of professional or research experience in data. Appreciate and leverage the complex environment in implementing translational solutions, with respect to the socio-economical, behavioral, and ethical considerations. Core competencies in use the underlying technologies of networking, security, and cloud computing. Deep understanding of the technical ecosystem. Effectively leverages the data, identity, and integration platforms to translate health IT solutions into the practice. Strong understanding of digital strategies for connecting with internal and external systems such as integrating external clinical applications with the EHR. Draws upon the social, cognitive and behavioral sciences to inform the design and evaluation of informatics solutions. Proficient in human factors design principles and how to apply to clinical informatics.

Preferred qualifications:

• Experience working with AI-assisted workflows, including validation and troubleshooting of AI-generated cohort definitions, queries, or data extraction logic; interest in agentic AI technologies is preferred.
• Understanding of Mayo Clinic data infrastructure and experience collaborating with clinicians, informaticians, and data scientists to deliver research data solutions
• Experience and/or knowledge of clinical trial operations, virtual/decentralized clinical trials, clinical research data collection workflows, and experience with trial emulation, simulation, or synthetic controls.
• Experience writing and optimizing SQL queries for cohort identification, data extraction, and query validation.
• Experience with Python (or similar scripting language) for data manipulation, workflow automation, and data quality assessment.
• Experience working with enterprise EHR data, relational databases, and clinical data warehouses, including familiarity with standard clinical terminologies (e.g., ICD-10, CPT, LOINC, RxNorm, SNOMED CT).
• Experience translating clinical research questions and eligibility criteria into reproducible cohort definitions, data queries, and extraction logic.
• Knowledge of descriptive statistics and exploratory data analysis to support cohort characterization, data validation, and quality assurance.