Data Scientist II

Axle is a bioscience and information technology company advancing translational research, biomedical informatics, and data science applications. The Data Scientist II will support the National Cancer Institute by developing reproducible bioinformatics workflows, analyzing multi-omics and clinical data, integrating data across lifecycle stages, and building researcher-facing dashboards, notebooks, reports, and APIs.

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

Preferred

advanced degree preferred

• Data Platform Experience: Experience building analytics solutions in platforms such as Snowflake, Databricks, or cloud data warehouses, with integrations across databases, APIs, dashboards, and application environments
• Bioinformatics Workflow Tooling: 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, is a plus

• Research and Application Enablement: Experience preparing curated datasets for dashboards, APIs, and web applications

Familiarity with Posit Connect, R/Shiny, Streamlit, Jupyter, or similar platforms is a plus

• Cloud, HPC, Storage, and Automation: 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

• Biomedical Domain Knowledge: 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

• Governance and Reproducibility: Experience with metadata management, data lineage, open-source code release, containerized analyses, and secure handling of de-identified or access-controlled research datasets
• Training and Scientific Enablement: 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

Data Platform Experience

Bioinformatics Workflow Tooling

scverse Python ecosystem

spatial single-cell analysis

Application Enablement

Posit Connect

R/Shiny

Streamlit

Jupyter

HPC

Storage

AWS

relational databases

HPC environments

Biomedical Domain Knowledge

HL7/FHIR standards

CDISC standards

OMOP standards

Governance

Reproducibility

metadata management

data lineage

open-source code release

containerized analyses

secure data handling

Scientific Enablement

support tool adoption

Explain workflows 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

Healthcare Flexible Spending Account (FSA)

Parking Reimbursement Account (PRK)

Dependent Care Assistant Program (DCAP)

Transportation Reimbursement Account (TRN)

Company

All-in-one platform for dental practices

San Antonio, Texas, USA

http://meetaxle.com/

Founded in 2021

11-50 employees

Qualifications

• Education & Background: Bachelor's degree in Data Science, Bioinformatics, Computer Science, Biological Sciences, or a related field

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

• Collaboration & Communication: 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

• Domain Alignment: 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

Or equivalent experience

Advanced degree preferred

• Data Platform Experience: Experience building analytics solutions in platforms such as Snowflake, Databricks, or cloud data warehouses, with integrations across databases, APIs, dashboards, and application environments
• Bioinformatics Workflow Tooling: 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, is a plus

• Research and Application Enablement: Experience preparing curated datasets for dashboards, APIs, and web applications

Familiarity with Posit Connect, R/Shiny, Streamlit, Jupyter, or similar platforms is a plus

• Cloud, HPC, Storage, and Automation: 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

• Biomedical Domain Knowledge: 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

• Governance and Reproducibility: Experience with metadata management, data lineage, open-source code release, containerized analyses, and secure handling of de-identified or access-controlled research datasets
• Training and Scientific Enablement: 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

Required skills

Represents the skills you have, click on the tags, R, Bioinformatics, Multi-Omics Analysis, Statistical Modeling, Dimensionality Reduction, Clustering, Differential Expression Analysis, Pathway Analysis, Data Integration, Nextflow/WDL

Preferred skills

advanced degree preferred

Experience

Mid level