Data Standardization Intern

Location: Titusville

This job is with Johnson & Johnson, an inclusive employer and a member of my Gwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and Med Tech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn

more at

Job Function:
Career Programs

Job Sub     Function:
Non-LDP Intern/Co-Op

Job Category:
Career Program

All Job Posting Locations:
Titusville, New Jersey, United States of America

Job Description:

About Innovative Medicine
Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.
Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.
Learn more at  
We are recruiting  a highly motivated and detail‑oriented Summer Intern to support strategic initiatives focused on data standardization, data connectivity, and the development of AI‑ready datasets. The intern will contribute to foundational work that enhances data interoperability, improves data quality, and accelerates the creation of enterprise‑scale analytics and AI solutions. This role provides an excellent opportunity to gain hands‑on experience in data engineering, metadata standards, and AI enablement within a scientific and R&D context.

Key Responsibilities:

In This Role, You Will:

· Support ongoing    data standardization efforts   , including harmonization of data structures, formats, and terminologies across multiple scientific and operational data sources.

· Assist in the development and enhancement of    data connectivity frameworks    that improve interoperability between platforms, pipelines, and analytical systems.

· Contribute to the preparation of    AI‑ready datasets    by implementing best practices in schema management, metadata curation, lineage documentation, and quality assessment.

· Conduct exploratory data analyses to evaluate    data completeness, consistency, and harmonization needs   .

· Collaborate with cross‑functional partners-including Data Engineering, Data Governance, and AI/ML teams-to capture requirements and support delivery of standardized data assets.

· Document workflows, data definitions, technical decisions, and process improvements to support organizational knowledge sharing and operational scalability.

· Assist in prototyping or testing automation approaches for data validation, transformation, and standardization where appropriate.

Experience And Skills:

Required:

· Currently pursuing a bachelor's, master's or Ph.D's degree in    Data Science, Computer Science, Information Systems, Bioinformatics, Engineering   , or a related discipline.

· Foundational knowledge of    Python   , SQL, or equivalent programming languages for data manipulation and analysis.

· Understanding of core data concepts, including data models, schemas, metadata, ontologies, and data governance principles.

· Demonstrated interest in AI systems, data engineering, or machine learning workflows.

· Strong analytical and problem‑solving skills, with exceptional attention to detail.

· Effective communication skills and the ability to work both independently and collaboratively.
Preferred:

· Familiarity with scientific or clinical data standards such as    CDISC, FHIR, OMOP   , internal ontologies, or FAIR data principles.

· Exposure to modern cloud platforms (e.g.,    Azure, AWS   ) and data tooling.

· Experience with workflow orchestration tools (e.g., Nextflow, Airflow) or scientific data pipelines.

· Understanding of R&D, clinical, omics, or experimental data environments.
Permanently authorized to work in the U.S., must not require…