Clinical Research Data Specialist I - Ouyang Lab

Cedars-Sinai

Los Angeles (CA)

Hybride

USD 60 000 - 80 000

Plein temps

14 jours+

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Résumé du poste

Cedars-Sinai Medical Center seeks a Clinical Research Data Specialist I for the Ouyang Lab to manage data for assigned studies, design data collection forms, and ensure accurate data abstraction. You will support data processing, reporting, and regulatory compliance with FDA and IRB guidelines, maintaining patient confidentiality under HIPAA.

The role emphasizes data integrity, security, and collaboration with investigators to enable robust cardiovascular research and breakthrough analyses.

Qualifications

  • Bachelor's degree in a relevant field preferred.
  • 1 year of experience in related field.
  • Training in biostatistics, epidemiology, statistics, or related field preferred.
  • Analytical experience using Electronic Health Record (EHR) data, longitudinal data, and advanced clinical statistical models is preferred.

Responsabilités

  • Manages and maintains data for research studies, ensuring accuracy and security.
  • Processes and abstracts clinical data from sources for analysis and reporting.
  • Designs data collection forms and maintains record systems.
  • Produces project reports for studies and ensures regulatory compliance.
  • Keeps up with regulations and policies, including FDA and IRB requirements.
  • Maintains Good Clinical Practice (GCP) and HIPAA confidentiality.

Connaissances

Data management
Clinical data abstraction
Biostatistics
Regulatory compliance
EHR data analysis

Formation

Bachelor's degree in life sciences or health sciences

Description du poste

Job Description

Clinical Research Data Specialist I - Ouyang Lab

Grow your career at Cedars-Sinai!

Cedars-Sinai Medical Center has been named to the Honor Roll in U.S. News & World Report’s “Best Hospitals 2025-2026” rankings. When you join our team, you’ll have access to groundbreaking biomedical research facilities and world-class medical education programs. We take pride in hiring exceptional, dedicated professionals who are passionate about advancing healthcare. Our physicians, nurses, and staff reflect the culturally and ethnically diverse communities we serve and are united by a shared commitment to excellence. Together, we foster a dynamic, inclusive environment that drives innovation and supports the gold standard of patient care.

Are you ready to be a part of breakthrough research?

The Smidt Heart Institute reflects Cedars-Sinai's steadfast dedication to heart disease and research innovation giving patients access to the highest level of care. Year after year, thousands of people trust their hearts to Smidt Heart Institute at Cedars-Sinai. Our cardiologists, cardiac surgeons and niche care teams treat the full spectrum of heart disease and disorders, while our investigators continue to advance the field with groundbreaking, life‑saving research. From genetic counseling and targeted drug therapies to an increasing array of minimally invasive procedures, Cedars‑Sinai continues to stand at the forefront of technology, innovation and discovery improving patient outcomes.

Dr. Ouyang is a physician‑scientist and cardiologist with subspecialty expertise in non‑invasive imaging and technical background in programming, statistics, and deep learning. As a statistician by training, he has always been fascinated by how to understand, visualize, and interpret data. With the exponential rise in the amount of data being collected in clinical care, there are huge opportunities to apply the additional data to personalize care and improve diagnosis and treatment of cardiovascular disease. Dr. Ouyang's goal as an investigator is to have expertise in echocardiography and deep learning applied to medical imaging, while leveraging clinically generated data to inform scientific discovery. His previous work has demonstrated the ability of convolutional neural networks to identify systemic human phenotypes that modify cardiovascular risk previously thought to be not apparent on echocardiographic images and precision phenotyping of cardiac function with higher precision than human interpreters. As an advanced cardiac imager and cardiologist, Dr. Ouyang hopes to advance and enhance cardiovascular imaging.

What will you be doing in this role?

The Clinical Research Data Specialist I manages the data for assigned research studies. This will include, designing forms for data collection, clinical data abstraction, processing data, maintaining record systems, and producing project reports for studies. Ensures compliance with all federal and local agencies including the Food and Drug Administration (FDA) and local Institutional Review Board.

Primary Duties and Responsibilities:

  • Manages and maintains research data to ensure accuracy, integrity, and security of complex, large computerized records systems. This includes performing data searches and other related administrative tasks.
  • Processes clinical data using a range of computer applications and database systems to support cleaning and management of subject or patient data.
  • Designs forms for data collection and performs clinical data collection/abstraction.
  • Produces project reports for research studies.
  • Understands regulations, policies, protocols and procedures to control and maintain accurate records.
  • Ensures compliance with all federal and local agencies including the Food and Drug Administration (FDA) and local Institutional Review Board.
  • Maintains research practices using Good Clinical Practice (GCP) guidelines.
  • Maintains strict patient confidentiality according to HIPAA regulations and applicable law.
  • Participates in required training and education programs.
Qualifications

Education:

  • Bachelor's degree in a relevant field (e.g., life sciences, health sciences) preferred.

Experience:

  • 1 year of experience in related field.
  • Training in biostatistics, epidemiology, statistics, or a related field preferred. Analytical experience using Electronic Health Record (EHR) data, longitudinal data, and advanced clinical statistical models is preferred.
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