Data Scientist II

Key Responsibilities

Bioinformatics Workflow and Data Pipeline Development

• Design, build, and maintain reproducible pipelines for 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 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.

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.

Required Qualifications

• 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.
• Genuine interest in biomedical and translational research; awareness of data governance, privacy, and compliance requirements.

Preferred Qualifications

• Experience building analytics solutions in platforms such as Snowflake, Databricks, or cloud data warehouses.
• Experience with workflow and reproducibility tools: Galaxy, Terra, Nextflow/WDL, Snakemake, Singularity, CWL.
• Familiarity with the scverse Python ecosystem (Scanpy, Squidpy, SCIMAP, AnnData) and spatial single-cell analysis methods (PhenoGraph, Louvain/Leiden clustering, UMAP, Ripley's L statistic).
• Experience preparing curated datasets for dashboards, APIs, and web applications; familiarity with Posit Connect, R/Shiny, Streamlit, Jupyter.
• Experience with AWS (EC2, S3, Lambda), object storage, relational databases, scheduled jobs, API integrations, secure data movement; familiarity with HPC environments, SLURM/SGE, or NIH Biowulf.
• Background in biomedical research, clinical research, or healthcare analytics; familiarity with HL7/FHIR, CDISC, or OMOP standards.
• 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.