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 oncology clinical and research data. The intern works at the UHealth medical campus in Miami, Florida, supporting research activities across Sylvester’s research laboratories and the Sylvester Survivorship and Supportive Care Institute. Work includes processing and analyzing molecular, sociodemographic, psychosocial, patient-reported, lifestyle, wearable, other clinical and research data, and publicly available population- and neighborhood-level datasets. The position supports supervised data cleaning, documentation, harmonization, and integration; analytical and technical work connecting individual-level clinical, genomic, digital health, and patient-reported outcomes data with sociodemographic, neighborhood, and population-level data; and research on how clinical, behavioral, social, and population factors relate to cancer care and outcomes. The intern also supports the development and evaluation of AI-driven risk algorithms, analytic tools, dashboards, AI-enabled applications, digital health and precision medicine tools, supportive care software, and risk-tailored AI-powered interventions delivered through eHealth platforms. As a Casual Worker, the individual provides services on an irregular or flexible basis in response to fluctuating work demands and helps assigned departments meet deadlines. The role includes regular one-on-one and small-group meetings with mentors and program faculty to review data pipelines, troubleshoot methods, refine research questions, and clarify expectations.

Requirements

High school diploma or equivalent/relevant experience, certification, or license.
Minimum of 1 year of relevant experience.
Ability to learn new procedures, technologies, and protocols and adapt to changing priorities and work demands.
Ability to collaborate with others and contribute to a team environment.
Technical proficiency with office software, technology, and relevant computer applications.
Strong written and verbal communication skills.
Strong organization, communication, and team science skills.
Interest in using AI and computational and data visualization approaches in oncology, clinical research, precision medicine, cancer survivorship, digital health, lifestyle medicine, or biomedical data sciences.

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.
Develop foundational knowledge of oncology research, including sociodemographic, psychosocial, biobehavioral, lifestyle, wearable, patient-reported outcomes, intervention mechanisms, molecular, and digital health data.
Support supervised cleaning, documentation, harmonization, and integration of diverse clinical and research datasets.
Assist with analytical 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 delivery and influence outcomes.
Support the development and evaluation of AI-driven risk algorithms, analytic tools, dashboards, and AI-enabled applications for data visualization, interpretation, and use.
Support the 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 meetings with mentors and program faculty to review data pipelines, troubleshoot methods, refine research questions, and clarify role expectations.
Meet applicable goals and deadlines, communicate work progress and issues to supervising staff, collaborate with department staff and other parties, follow University and unit policies, and safeguard University assets.