Data Scientist, Lupus Nexus

Lupus Research Alliance Inc.•New York, NY

8d•$125,000 - $150,000

About The Position

The Data Scientist will be part of the Lupus Research Alliance’s Research Department and support efforts across the Lupus Nexus (LNx), an initiative integrating a patient registry, linked biorepository, and data analytics platform. The role manages and analyzes complex scientific and clinical datasets to enable high-quality, reproducible research and biological insights for researchers accessing the LNx. The Data Scientist will build and manage scientific data pipelines, conduct code-based analyses of multi-omics, clinical, and biomarker data, and interpret findings in an immunological and disease context. The position partners directly with researchers to translate scientific questions into analytical approaches while maintaining rigorous standards for data accuracy and reproducibility. The role may also lead or contribute to publications and data reports demonstrating what LNx data can answer.

Requirements

PhD in immunology, biomedical sciences, biostatistics, epidemiology, computational biology, bioinformatics, or a related field, with at least 3 years of relevant experience; or an MS with at least 8 years of relevant experience.
Experience analyzing both clinical and multi-omic or molecular data.
Working knowledge of R Studio and Jupyter Notebook.
Proficiency in at least one programming language commonly used for scientific analyses, such as R or Python, including the ability to write, modify, debug, and maintain analytical code.
Experience working with human-subjects data under consent, IRB, and privacy requirements such as HIPAA.
Ability to think strategically when planning and managing responsibilities.
Hands-on, self-directed, detail-oriented, organized, and conscientious approach to work.
Ability to explain data, methods, and limitations clearly to researchers.
Strong scientific writing skills, including writing methods sections for abstracts and publications.
Ability to work independently and prioritize and manage multiple activities within tight timelines.
Ability to build excellent working relationships internally and externally and contribute as a team player.

Nice To Haves

Experience with longitudinal clinical or real-world data.
Working knowledge of Nextflow and Docker.
Working knowledge of AWS environments, SQL, and GitHub or another version-control system.
Experience with observational research methods and clinical or real-world data standards such as OMOP or CDISC.
Experience with immunology or autoimmune disease data.

Responsibilities

Manage and oversee scientific data analysis pipelines, ensuring the accuracy, consistency, and completeness of scientific datasets received from vendors and external researchers.
Oversee metadata management and data harmonization to facilitate standardized data sharing, and contribute to resolving and documenting data queries under the LNx Data Governance and Management Plan.
Contribute to review and quality-control assessments of scientific data by the LNx Scientific Review Committee.
Work with internal staff and external vendors to integrate clinical and research data across platforms and ensure interoperability.
Engage collaborators and business partners to identify data needs, improve workflows, and enhance research data usability.
Independently develop and execute code-based analyses of complex clinical, immunological, and multi-omics datasets to address researchers’ questions.
Develop reproducible analytical workflows and document code, methods, assumptions, and outputs to support scientific transparency and reuse.
Identify opportunities to use existing datasets and pipelines to address new scientific questions.
Use available clinical and scientific data to help researchers assess feasibility and refine inclusion and exclusion criteria for research projects.
Serve as a computational scientific resource, helping researchers understand how available datasets can address their questions.
Lead or contribute to abstracts, publications, and an annual data report demonstrating what LNx data can answer.
Manage data access requests and review processes, ensuring compliance with governance policies and responsible data sharing.
Establish and manage procedures for scientific data provenance and governance.
Contribute to policies and procedures for ethical and effective release of scientific data to researchers and registry participants, including data-sharing compliance, harmonization, and metadata management.
Apply disclosure controls before releasing results to researchers or participants.