Manager, Data Science - Informatics and Health Data Science

University of Miami•Miami, FL

1d•Onsite

About The Position

The University of Miami/UHealth Department of Informatics and Health Data Science has an exciting opportunity for a full time Manager, Data Science to work in Miami, FL. The Manager, Data Science supports the Department Chair in managing and coordinating data science initiatives across the Department of Informatics and Health Data Science (DIHDS) and the Media and Innovation Laboratory (The MIL). The Manager applies advanced analytics, artificial intelligence, machine learning, predictive modeling, and related data-driven technologies to support the department’s strategic priorities across Research, Education, Clinical Innovation, Service/Outreach/Policy/Ethics, and Venture. A major departmental priority is the development and implementation of large-scale, multidisciplinary research initiatives supported by the National Institutes of Health (NIH), the Advanced Research Projects Agency for Health (ARPA-H), and other federal, foundation, and industry sponsors. The Manager works closely with the Chair, faculty, investigators, engineers, technical teams, and collaborators across the University of Miami to address the data architecture, integration, analytics, modeling, infrastructure, and reporting requirements of complex, multi investigator, and multi-institutional projects. The Manager also supports key departmental engineering and innovation initiatives, including the continued development, implementation, evaluation, and advancement of the MIL Agentic Data Scientist (MILADS) and other AI-enabled research tools. In collaboration with departmental leadership and internal and external partners, the Manager contributes technical expertise to the development, validation, translation, and potential commercialization of innovative products and technologies.

Requirements

Bachelor’s degree in relevant field required.
Minimum 3 years of relevant experience required.
Operational Management: Optimizes day-to-day operations and processes for efficiency and effectiveness.
Organizational Development: Ability to implement strategies to improve organizational effectiveness, engagement, and manage change.
Financial Oversight: Knowledge of financial operations and management.
Team Leadership: Ability to create and maintain a cohesive and productive team environment, build positive working relationships and work collaboratively with others.
Technical Proficiency: Skilled in using office software, technology, and relevant computer applications.
Communication: Strong verbal and written communication skills to convey ideas clearly and persuasively.

Responsibilities

Conducts initial assessments and data analyses to identify and quantify/qualify recommended opportunities for improvement.
Serves as a business change agent leading process re-engineering activities/project.
Manages the daily activities of the team responsible for identifying business trends and problems through big data analysis.
Oversees the interpretation of results using a variety of techniques, ranging from simple data aggregation via statistical analysis to complex data mining.
Manages the design and implementation of big data solutions for the organization utilizing big data tools.
Applies and integrates statistical, mathematical, predictive modeling and business analysis skills to manage and manipulate complex high-volume data from a variety of sources.
Develops infrastructure systems that connect internal data sets.
Manages analysis of large quantities of data and presents insights and predictions to support management planning, execution and monitoring of business decisions.
Manages team, budget, and project staffing assignments.
Establishes and continuously assesses the effectiveness of the internal controls within the unit and compliance with University policies and procedures.
Ensures employees are trained on controls within the function and on University policy and procedures.
Manages and coordinates the department’s data science portfolio in alignment with the strategic priorities of DIHDS, The MIL, the Miller School of Medicine, and the University of Miami.
Works closely with the Department Chair, faculty, investigators, engineers, and research teams to translate scientific, operational, and population-health questions into defined data science projects, analytical plans, technical requirements, and measurable outcomes.
Supports the development of large-scale NIH, ARPA-H, and other externally sponsored proposals by contributing to study design, data strategies, preliminary analyses, technical plans, analytic workflows, infrastructure requirements, budgets, and related application components.
Provides ongoing data science and analytical support for funded studies, including data acquisition, ingestion, cleaning, harmonization, integration, documentation, analysis, visualization, and reporting.
Manages data science personnel, analysts, consultants, and other technical contributors assigned to departmental research, education, engineering, and innovation initiatives.
Coordinates the development and application of advanced analytical methods, including statistical analysis, predictive modeling, machine learning, classification, time-series analysis, causal inference, feature engineering, and model-performance evaluation.
Supports the design, development, implementation, evaluation, and continued advancement of MILADS and related artificial intelligence and agentic-AI tools that accelerate data ingestion, analysis, interpretation, reporting, and research translation.
Develops and maintains scalable and reproducible data science workflows, analytical pipelines, reusable code, data dictionaries, technical documentation, dashboards, visualization tools, and reporting systems.
Establishes and maintains departmental practices for evaluating the accuracy, reliability, fairness, reproducibility, transparency, interpretability, privacy, security, and practical value of statistical models and AIenabled solutions.
Collaborates with University information technology, data-governance, cybersecurity, privacy, compliance, and research-administration teams to ensure that departmental data science activities comply with institutional policies, sponsor requirements, and applicable regulations.
Provides data science support for research protocols, progress reports, manuscripts, conference presentations, technical reports, demonstrations, and other scholarly or professional products.
Supports the department’s education and workforce-development activities by developing workshops, guided working sessions, technical resources, and mentoring opportunities that strengthen data literacy, computational skills, and the responsible use of artificial intelligence.
Coordinates data science contributions to departmental programs such as MILADS, NHLBI-funded PRIDE, biomedical informatics, digital health, precision medicine, and other research and educational initiatives.
Supports key engineering initiatives by coordinating data requirements, analytical methods, model development, technical validation, documentation, and performance evaluation across multidisciplinary project teams.
Supports the department’s Venture vertical by contributing data science expertise to the design, development, testing, validation, and translation of innovative products, platforms, prototypes, and technologies.
Collaborates with University technology-transfer resources and external partners to support product commercialization, intellectual-property development, industry engagement, and the transition of research innovations into scalable solutions.
Develops and maintains collaborations with academic units, healthcare organizations, technology companies, community partners, government agencies, and other external stakeholders to expand access to data, expertise, technologies, funding, and translational opportunities.
Evaluates emerging data science, machine-learning, generative-AI, and agentic-AI technologies and recommends appropriate tools, platforms, methodologies, and investments for departmental consideration.
Establishes performance indicators and evaluation frameworks to measure the utilization, effectiveness, scientific contribution, operational value, scalability, and impact of departmental data science initiatives.
Performs other duties consistent with the scope, responsibilities, qualifications, and strategic priorities of the position.