Data Scientist II

(ID: 2026-2574)

Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to research centers and healthcare organizations nationally and abroad. With experts in biomedical science, software engineering, and program management, we focus on developing and applying research tools and techniques to empower decision-making and accelerate research discoveries.

We work with some of the top research organizations and facilities in the country including multiple institutes at the National Institutes of Health (NIH).

Key Responsibilities

• Bioinformatics Workflow and Data Pipeline Development: Design, build, and maintain reproducible pipelines for diverse biomedical data types — including genomic, transcriptomic, single-cell, spatial, proteomic, metagenomic, metabolomic, and clinical datasets. Develop reusable transformation logic and curated datasets supporting analytics, dashboards, APIs, notebooks, and downstream research workflows.
• Multi-Omics Analysis: Support NCI CBIIT labs in their analysis workflows including bulk RNA-seq (QC, DEG, GSEA), single-cell RNA-seq (clustering, UMAP/t-SNE, cell type annotation, DEG), and Digital Spatial Profiling (annotation, QC, normalization, spatial deconvolution, volcano plots, heatmaps).
• Data Integration and Lifecycle Support: Enable reliable data movement from source systems into structured, analysis-ready formats. Support ingestion, curation, metadata capture, source-to-target mapping, schema management, provenance tracking, and long-term maintainability of data products.
• Statistical Modeling and Machine Learning: Apply statistical and ML methods — including hypothesis testing, regression, clustering, PCA, UMAP, t-SNE, and classification — to biomedical datasets. Incorporate AI/LLM-based extraction where appropriate, with clear validation and communication to stakeholders.
• Researcher-Facing Applications and Visualization: Build and support interactive dashboards (Shiny, Streamlit), notebooks, reports, and APIs enabling researchers to explore multi-omics and clinical data. Support figure generation for QC, differential expression, pathway, and spatial analyses.
• Collaboration: Partner with data scientists, bioinformaticians, researchers, developers, and government stakeholders to translate scientific needs into technical specifications, data models, and reusable workflows that accelerate biomedical research.
• Required Qualifications
• Education & Background: Bachelor's degree in Data Science, Bioinformatics, Computer Science, Biological Sciences, or a related field (advanced degree preferred), or equivalent experience. Demonstrated experience in a data-intensive role supporting biomedical research or scientific computing.
• Data Science and Bioinformatics Expertise: Strong proficiency in Python and R for analysis, scripting, and visualization. Hands-on experience with at least two omics data types (e.g., bulk RNA-seq, scRNA-seq, spatial transcriptomics, proteomics, metagenomics, GWAS).
• Analytical Skills: Solid understanding of statistical modeling, dimensionality reduction, clustering, differential expression, and pathway analysis. Ability to work with structured, semi-structured, and unstructured data across relational and data lake environments.
• Strong problem-solving skills with the ability to communicate effectively across technical and non-technical audiences. Able to translate scientific needs into technical solutions and clearly articulate risks, assumptions, and limitations.