Data Scientist II

Summary

Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications. They are seeking a Data Scientist II to support the National Cancer Institute at the NIH, focusing on developing computational infrastructure for cancer research and collaborating with various stakeholders to enhance biomedical research.

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

Skills

• 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
• 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
• 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
• Experience with workflow and reproducibility tools used in Galaxy, Terra, Nextflow/WDL, Snakemake, Singularity, or CWL
• 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
• Experience preparing curated datasets for dashboards, APIs, and web applications
• Familiarity with Posit Connect, R/Shiny, Streamlit, Jupyter, or similar platforms
• 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
• 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
• 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

Qualifications

Must Haves

• 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
• 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
• 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

Nice to Haves

• Experience building analytics solutions in platforms such as Snowflake, Databricks, or cloud data warehouses, with integrations across databases, APIs, dashboards, and application environments
• Experience with workflow and reproducibility tools used in Galaxy, Terra, Nextflow/WDL, Snakemake, Singularity, or CWL
• 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
• Experience preparing curated datasets for dashboards, APIs, and web applications
• Familiarity with Posit Connect, R/Shiny, Streamlit, Jupyter, or similar platforms
• 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
• 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
• 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

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)