Develop state-of-the art agentic workflows, build agents capable of QC, tool selection, execution optimization, and results interpretation
Define and execute QC, benchmarking and validation strategies for pipelines and algorithms; perform evaluation and assessment of technical wetlab platforms through data; produce metrics and reports documenting performance and limitations.
Collaborate with internal biomarker labs and CROs/vendors to onboard new assays; author and maintain data transfer specifications, interface control documents, and acceptance criteria. Ensure cross functional coordination and data integrity.
Produce high-quality technical documentation, statistical methods descriptions, and contribute methods sections for reports, regulatory submissions and publications.
Troubleshoot and resolve pipeline issues and collaborate with cross-functional teams to deliver timely results.
Required qualifications
Education & experience
Master’s or PhD in Bioinformatics, Computational Biology, Genetics/Genomics, Computer Science, Statistics or a related discipline.
~8+ years of relevant hands-on experience in bioinformatics or computational biology, including experience designing and delivering production or research pipelines for NGS/omics data.
Technical skills
Strong programming skills in Python and R. Familiarity with other languages (C/C++, Java) is a plus.
Deep understanding of biomarker assays and sequencing technologies (immunoassay, flow cytometry, immunohistochemistry, proteomics, whole genome sequencing, exome sequencing, targeted panel sequencing, bulk RNA-seq, methylation, metabolomics)
Hands-on experience with NGS analysis workflows (alignment, variant calling, RNA-seq, single-cell, fusion detection, copy number, structural variants) and with downstream statistical methods for omics data (pathway annotation, GSVA/GSEA, DE analysis).
Experience with single cell RNA-seq and spatial transcriptomics data is a plus.
Practical experience with workflow engines (Nextflow, Snakemake) and containerization (Docker/Singularity).
Experience with cloud (AWS) and HPC environments and scalable compute frameworks. Experience using GPU is a plus.
Solid understanding of data formats and standards (FASTQ, BAM/CRAM, VCF/MAF, HDF5/AnnData/Seurat) and metadata best practices.
Deep knowledge of public genomics, transcriptomics, proteomics, and clinical databases, including TCGA, GTEx, GEO, SRA, dbGaP, cBioPortal, ClinVar, COSMIC, gnomAD, and UniProt, to support biomarker discovery, genomic interpretation, and translational research.
Experience and expertise with deep learning models, foundation models, large language models (LLMs), RAG, and agentic AI workflows is preferred.
Familiarity with version control (Git), CI/CD, automated testing, and reproducible research practices.
Strong statistical reasoning and experience applying appropriate models and QC for biological data.
Experience with benchmarking and validation frameworks, unit/integration testing for pipelines.
Prior experience in a biomedical/pharmaceutical environment
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