Data Scientist, Lupus Nexus Analytics

Lupus Research Alliance Inc.; New York (NY); Hybrid; USD 125,000 - 150,000; Full time

Job summary

The Lupus Research Alliance seeks a Data Scientist to support the Lupus Nexus initiative, integrating a patient registry with a biorepository and a data analytics platform. You will manage and analyze complex datasets to generate reproducible insights for researchers.

The role involves building data pipelines, performing code-based analyses of multi-omics and clinical data, and collaborating with researchers to translate questions into analytical approaches while maintaining high data quality.

Qualifications

• PhD in immunology or related field with 3+ years of experience, or MS with 8+ years.
• Experience analyzing clinical and multi-omic data; longitudinal data preferred.
• Proficiency in R and Python; SQL and GitHub for version control.
• Familiarity with OMOP/CDISC and HIPAA/privacy standards preferred.
• Strong writing and ability to explain methods to researchers.

Responsibilities

• Oversee scientific data analysis pipelines and data quality.
• Develop reproducible analytical workflows and document methods.
• Analyze complex clinical, immunology, and multi-omics data.
• Collaborate with researchers to translate questions into analyses.
• Lead or contribute to abstracts, publications, and data reports.

Skills

R Studio

Jupyter Notebook

Python

R

GitHub

SQL

Docker

Nextflow

AWS

Education

PhD in immunology or related

MS +8 years experience

Tools

Docker

Nextflow

AWS

Job description

The Lupus Research Alliance seeks a Data Scientist to support the Lupus Nexus initiative, integrating a patient registry with a biorepository and a data analytics platform. You will manage and analyze complex datasets to generate reproducible insights for researchers.

The role involves building data pipelines, performing code-based analyses of multi-omics and clinical data, and collaborating with researchers to translate questions into analytical approaches while maintaining high data quality.