Bioinformatics Scientist

Job Description

We are seeking an experienced Bioinformatics Scientist to design, automate, implement, validate, and execute advanced bioinformatics analyses and reproducible computational pipelines supporting biomarker analysis, translational research, and clinical development.

This individual contributor position combines expertise in next-generation sequencing (NGS), multi-omics analysis, single-cell and spatial omics, data science, artificial intelligence, and software engineering. The successful candidate will translate complex biological datasets into robust, reproducible, and scientifically reliable results for computational biologists, translational scientists, data engineers, and clinical teams.

The role also involves developing scalable analytical frameworks, optimizing production-grade pipelines, implementing quality control and validation strategies, and exploring AI-driven agentic workflows for automated quality assessment, tool selection, execution optimization, and results interpretation.

Key Responsibilities

• Design, develop, validate, automate, and maintain end-to-end bioinformatics pipelines for genomics, transcriptomics, single-cell and spatial omics, proteomics, epigenomics, minimal residual disease (MRD), metabolomics, and related biomarker assays.
• Implement reproducible workflows using Nextflow, Snakemake, Docker, and related technologies, incorporating provenance tracking, version control, and CI/CD.
• Optimize production-grade pipelines for runtime, memory utilization, scalability, reproducibility, and cost-effectiveness.
• Develop scalable multi-omics integration frameworks combining genomic, transcriptomic, proteomic, imaging, and clinical data for biomarker discovery, patient stratification, predictive modeling, and drug development.
• Develop AI-driven agentic workflows for quality control, tool selection, execution optimization, and results interpretation.
• Establish quality control, benchmarking, and validation strategies for analytical pipelines and algorithms.
• Evaluate technical wet-lab platforms using analytical data and generate performance metrics and reports documenting limitations.
• Collaborate with biomarker laboratories, contract research organizations (CROs), and external vendors to onboard new assays and define data transfer specifications, interface control documents, and acceptance criteria.
• Maintain data integrity and coordinate deliverables across cross-functional teams.
• Prepare technical documentation and statistical methods descriptions, contributing to reports, regulatory submissions, and scientific publications.
• Troubleshoot pipeline issues and ensure timely delivery of analytical results.

Qualifications

• Master's or PhD in Bioinformatics, Computational Biology, Genetics, Genomics, Computer Science, Statistics, or a related discipline.
• Strong foundation in computational biology, statistical analysis, genomics, and multi-omics data interpretation.
• Experience working with biological datasets and reproducible computational workflows.

Experience

Approximately 8 or more years of relevant hands-on experience in bioinformatics or computational biology.

Demonstrated experience designing and delivering production or research pipelines for NGS and omics datasets.

Experience in biomedical or pharmaceutical environments.

Practical experience with pipeline validation, benchmarking, reproducibility, and scalable computational workflows.

Skills Required

• Programming: Python and R; familiarity with C/C++ or Java is an advantage.
• NGS analysis: Sequence alignment, variant calling, bulk RNA-seq, single-cell analysis, fusion detection, copy-number analysis, and structural variant analysis.
• Omics statistics: Differential expression analysis, pathway annotation, GSVA, GSEA, and statistical quality control.
• Biomarker technologies: Immunoassays, flow cytometry, immunohistochemistry, proteomics, whole-genome sequencing, whole-exome sequencing, targeted sequencing panels, methylation, and metabolomics.
• Single-cell and spatial omics: Experience with scRNA-seq and spatial transcriptomics is an advantage.
• Workflow engineering: Nextflow, Snakemake, Docker, and Singularity.
• Computing infrastructure: AWS cloud, high-performance computing (HPC), scalable computing frameworks, and GPU technologies.
• Data formats and standards: FASTQ, BAM/CRAM, VCF/MAF, HDF5, AnnData, Seurat, and biological metadata standards.
• Biological databases: TCGA, GTEx, GEO, SRA, dbGaP, cBioPortal, ClinVar, COSMIC, gnomAD, and UniProt.
• AI and machine learning: Deep learning, foundation models, large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI workflows.
• Software engineering: Git, CI/CD, automated testing, unit and integration testing, and reproducible research practices.
• Scientific communication: Technical documentation, statistical reporting, troubleshooting, and cross-functional collaboration.

Additional Information

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