Data Science & AI Intern

University of Miami•Miami, FL

4d•Onsite

About The Position

The Sylvester Data Science and AI Innovations Internship Program provides supervised training for students, trainees, research fellows, and others interested in developing tools to analyze and visualize clinical and research data in oncology. The intern will process and analyze molecular, sociodemographic, psychosocial, patient-reported, lifestyle, wearable, and other clinical and research data, as well as publicly available population- and neighborhood-level datasets. Work takes place across Sylvester research laboratories and the Sylvester Survivorship and Supportive Care Institute. The position supports supervised data cleaning, documentation, harmonization, and integration of varied datasets, as well as research and technical work connecting individual-level clinical, genomics, digital health, and patient-reported outcomes data with population- and neighborhood-level information. The intern contributes to research on how clinical, behavioral, social, and population factors affect cancer care and outcomes, and supports the development and evaluation of AI-driven algorithms, analytics, dashboards, applications, digital health tools, precision medicine tools, and supportive care interventions. The role includes regular individual and small-group meetings with mentors and program faculty. The posting describes the role as a casual worker who provides services on an irregular or flexible basis to meet fluctuating University work demands and help departments meet deadlines. It also lists the job status as full time and the employee type as temporary-intermittent.

Requirements

High school diploma or equivalent/relevant experience, certification, or license.
Minimum one year of relevant experience.
Ability to learn new procedures, technologies, and protocols and adapt to changing priorities and work demands.
Ability to work collaboratively and contribute to a team environment.
Proficiency with office software, technology, and relevant computer applications.
Strong written and verbal communication skills.
Interest in applying AI and computational and data visualization approaches in oncology, clinical research, precision medicine, cancer survivorship, digital health, lifestyle medicine, and biomedical data sciences.
Strong organization, communication, and team science skills.

Nice To Haves

Experience or coursework in Python, R, SQL, JavaScript, statistics, data visualization, software development, or related tools.
Experience with Git, reproducible workflows, software testing, or application development.
Familiarity with cloud-based development environments such as Azure.
Familiarity with developing, training, and evaluating AI models.

Responsibilities

Perform assigned tasks and projects and provide research, professional, technical, or clerical support to the assigned division.
Process and analyze clinical and research data, including molecular, sociodemographic, psychosocial, patient-reported, lifestyle, wearable, and publicly available population- and neighborhood-level data.
Support supervised cleaning, documentation, harmonization, and integration of diverse clinical and research datasets.
Assist with analytic and technical activities linking individual-level clinical, genomics, digital health, and patient-reported outcomes data with sociodemographic, neighborhood, and population-level data.
Contribute to research examining how clinical, behavioral, social, and population-level factors guide cancer care and influence outcomes across cancer sites and treatment modalities.
Support development and evaluation of AI-driven risk algorithms, analytic tools, dashboards, and AI-enabled applications for visualization, interpretation, and use of clinical and research data.
Support development and evaluation of digital health, precision medicine, and supportive care tools and software.
Assist in developing and implementing risk-tailored, AI-powered adjunctive supportive care interventions delivered through eHealth platforms.
Participate in regular one-on-one and small-group meetings with mentors and program faculty to review data pipelines, troubleshoot methods, refine research questions, and clarify role expectations.
Meet applicable goals and deadlines, keep supervising staff informed of work progress and issues, collaborate with department staff and other parties, and follow University and unit policies while safeguarding University assets.