Data Scientist II

About the role

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. We focus on developing and applying research tools and techniques to empower decision-making and accelerate research discoveries.

Responsibilities

• 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.
• 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).
• 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.
• 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.
• 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.
• Collaborate 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.

Requirements

• Bachelor's degree in Data Science, Bioinformatics, Computer Science, Biological Sciences, or a related field (advanced degree preferred), or equivalent experience.
• 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).
• 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.

Qualifications

• Genuine interest in biomedical and translational research. Ability to quickly learn domain-specific terminology and workflows, with awareness of data governance, privacy, and compliance requirements for clinical and research data.
• Experience building analytics solutions in platforms such as Snowflake, Databricks, or cloud data warehouses, with integrations across databases, APIs, dashboards, and application environments.
• Familiarity with the scverse Python ecosystem (Scanpy, Squidpy, SCIMAP, AnnData) and spatial single-cell analysis methods, including PhenoGraph, Louvain/Leiden clustering, UMAP, and Ripley's L statistic, is a plus.
• Experience preparing curated datasets for dashboards, APIs, and web applications. Familiarity with Posit Connect, R/Shiny, Streamlit, Jupyter, or similar platforms is a plus.
• Experience with AWS (EC2, S3, Lambda), object storage, relational databases, scheduled jobs, API integrations, and secure data movement. Familiarity with HPC environments, SLURM/SGE, or NIH Biowulf is preferred.
• Background in biomedical research, clinical research, or healthcare analytics. Familiarity with standards such as HL7/FHIR, CDISC, or OMOP, and experience with clinical, genomic, or biospecimen data is a plus.
• Experience with metadata management, data lineage, open-source code release, containerized analyses, and secure handling of de-identified or access-controlled research datasets.
• Experience creating documentation, training materials, or workshops for researchers and non-coder audiences. Ability to support tool adoption and explain workflows and results clearly is strongly preferred.

Skills

• Python
• R
• Scalable computing
• Data integration
• Statistical modeling
• Machine learning
• Workflow management
• Visualization
• Collaboration

Benefits

• 100% Medical, Dental & Vision Coverage for Employees
• Paid Time Off and Paid Holidays
• 401K match up to 5%
• Educational Benefits for Career Growth
• Employee Referral Bonus
• Flexible Spending Accounts:Healthcare (FSA)
• Parking Reimbursement Account (PRK)
• Dependent Care Assistant Program (DCAP)
• Transportation Reimbursement Account (TRN)

Pay

$130,000 USD - $145,000 USD

Schedule

Full-time