Data Scientist - Urology

UCLA Health • Full-time • Los Angeles, California, United States • $33 - $69 / hour • 3d ago

Description

Drive Impactful Healthcare Research Through Data Science and Analytics

The David Geffen School of Medicine at UCLA, Department of Urology is seeking a full-time Data Scientist to support transformative research initiatives that improve healthcare delivery and population health. This role is ideal for professionals who combine programming, statistical expertise, and domain knowledge to solve complex problems and guide data-informed decision-making.

As a key member of our research team, you’ll work with faculty and staff to analyze large healthcare datasets, build predictive models, and communicate insights that shape clinical practice and policy.

We’re looking for someone who thrives in a collaborative, fast-paced environment, brings strategic thinking and technical expertise, and is passionate about using data to improve lives.

Join us in shaping the future of healthcare through data science and innovation. Apply today and become part of a dynamic team at the forefront of population-health research.

Hourly Salary Range: $33.96 - $69.54

Qualifications

Required:

Bachelor’s degree in Statistics/Computer Science/Bioinformatics/Data Specification/ Data Science/Computational or equivalent combination of education and experience.
1-2 years of experience in Statistics/Computer Science/Bioinformatics/Data Specification/ Data Science.
Knowledge of basic and multivariate statistics.
Knowledge of research design.
Competency in statistical software applications (e.g., R, SAS, STATA).
Ability to conceptualize and construct tables and graphs.
Working knowledge of statistical and/or medical terminology.
Ability to summarize statistical analyses for collaborators.
Knowledge of statistical techniques.
Ability to utilize database management software to run queries and extract data.
Ability to construct databases.
Ability to produce visual tools and dashboards that highlight the essential information from a project or study.
Knowledge of basic office software and applications (e.g., Microsoft Office Suite, Google, Internet search engines, etc.).
Ability to learn new statistical techniques and to adapt existing code to customize statistical analysis strategies.