Bioinformatics Scientist

We are seeking a strong Bioinformatics Scientist to design, automate, implement, validate, and execute advanced bioinformatics analyses and reproducible pipelines that support biomarker analysis, translational research, and clinical development. This individual contributor role combines deep technical expertise in NGS/omics analyses, scRNA/spatial omics, data science, modern AI technologies and software engineering with strong scientific judgment to translate biological data into robust, reproducible results used by computational biologists, translational scientists, data engineers and clinical teams.

• →Design, develop, validate, automate and maintain end-to-end bioinformatics analysis pipelines and tools for genomics, transcriptomics, single-cell, spatial omics, proteomics, epigenomics, MRD, metabolomics, and related biomarker assays.
• →Implement reproducible workflows using workflow engines (e.g., Nextflow, Snakemake) and containerization (e.g., Docker); ensure provenance, versioning and CI/CD for pipelines.
• →Lead the design and optimization of production-grade bioinformatics pipelines and analytical workflows, driving improvements in runtime efficiency, memory footprint, scalability, reproducibility, and cost-effectiveness for large-scale genomic and multi-omics analyses.
• →Design and implement scalable multi-omics integration frameworks that combine genomic, transcriptomic, proteomic, imaging, and clinical data to enable biomarker discovery, patient stratification, predictive modeling, and data-driven decision-making in drug development and clinical trials.
• →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.

• 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.

• 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