Biomedical Omics Data Scientist II

Axle Energy; Greater London; On-site; GBP 96,000 - 107,000; Full time

Benefits offered by this job

Medical coverage

Paid time off

401K match

Educational benefits

Referral bonus

FSAs: Healthcare

Parking reimbursement

Dependent care

Transportation reimbursement

Job summary

Axle Energy seeks a Data Scientist II to advance cancer research at NIH/NCI CBII/T through building scalable computational infrastructure. You will handle full omics data lifecycles, integrate modalities, and collaborate with scientists, data engineers, and government stakeholders to ensure reproducible, containerized, and well-documented workflows.

Responsibilities include designing pipelines across genomic, transcriptomic, and clinical datasets, applying statistical and ML methods, and

Qualifications

• BS/BA in data science, bioinformatics, CS or related; advanced degree preferred.
• Strong Python and R for analysis, scripting, visualization.
• Experience with multiple omics data types and biomedical data platforms.

Responsibilities

• Design and maintain reproducible bioinformatics pipelines for diverse data types.
• Perform multi-omics analyses (RNA-seq, scRNA-seq, spatial transcriptomics, proteomics).
• Develop data integration workflows and FAIR-compliant infrastructure.
• Build researcher-facing dashboards, notebooks, and reports for data exploration.

Skills

Python

R

Omics data analysis

Education

Bachelor's degree or higher in Data Science/Bioinformatics/CS

Tools

Galaxy

Terra

Nextflow/WDL

Snakemake

Singularity

Job description

Axle Energy seeks a Data Scientist II to advance cancer research at NIH/NCI CBII/T through building scalable computational infrastructure. You will handle full omics data lifecycles, integrate modalities, and collaborate with scientists, data engineers, and government stakeholders to ensure reproducible, containerized, and well-documented workflows.

Responsibilities include designing pipelines across genomic, transcriptomic, and clinical datasets, applying statistical and ML methods, and