Data Scientist II, Biomedical Informatics & Multi-Omics

Axle; United States; A distancia; USD 105.000 - 155.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Medical, dental, vision coverage

Paid time off

401K match

Educational benefits

Referral bonus

FSA

Parking reimbursement

DCAP

Transit reimbursement

Descripción de la vacante

Axle is seeking a Data Scientist II to join the NIH CBII T at NCI in Rockville, MD. You will build computational infrastructure to support omics data lifecycles, integrate modalities, and enable reproducible analytics across research projects.

You will work with biologists, clinicians, data engineers, and software developers to create scalable pipelines, dashboards, and APIs that empower cancer research and FAIR data practices for long-term reuse.

Formación

• Bachelor's degree in Data Science, Bioinformatics, Computer Science, Biological Sciences, or related field; advanced degree preferred.
• Strong proficiency in Python and R for analysis, scripting, and visualization.
• Experience with multiple omics data types (bulk RNA-seq, scRNA-seq, spatial transcriptomics, etc.) and statistical analysis.

Responsabilidades

• Design, build, and maintain reproducible pipelines for diverse biomedical data types.
• Support multi-omics analysis including RNA-seq, spatial transcriptomics, and proteomics workflows.
• Enable data movement and lifecycle support from source systems to analysis-ready formats.
• Apply statistical modeling and ML methods to biomedical datasets and communicate results.
• Develop researcher-facing apps and dashboards (Shiny, Streamlit) for data exploration.
• Collaborate with scientists, engineers, and stakeholders to translate needs into workflows.

Conocimientos

Python

R

Data analysis

Educación

Bachelor's degree in data science / bioinformatics

Herramientas

Shiny

Streamlit

Descripción del empleo

Axle is seeking a Data Scientist II to join the NIH CBII T at NCI in Rockville, MD. You will build computational infrastructure to support omics data lifecycles, integrate modalities, and enable reproducible analytics across research projects.

You will work with biologists, clinicians, data engineers, and software developers to create scalable pipelines, dashboards, and APIs that empower cancer research and FAIR data practices for long-term reuse.