Apply on Employer Site

Inside Higher Ed · 4 hours ago

Bioinformatics Engineer II (18 Month Fixed-Term) (Hybrid Opportunity)

Stanford, CA

Full-time

Hybrid

Mid Level

$108K/yr - $128K/yr

3+ years exp

Stanford University is seeking a talented Bioinformatics Engineer II to join the Bioinformatics Core of the Molecular Transducers of Physical Activity Consortium. In this role, you will focus on genome, epigenome, and transcriptome analyses, running pipelines and tools to convert raw sequencing data into analysis-ready results, while collaborating with a multidisciplinary team to advance personalized exercise science and public health.

Digital MediaEducationHigher EducationJournalismRecruiting

Responsibilities

Prioritize and extract data from a variety of sources such as notes, survey results, medical reports, and laboratory data, and maintain its accuracy and completeness

Determine additional data collection and reporting requirements

Design and customize reports based upon data in the database. Oversee and monitor regulatory compliance for utilization of the data

Use system reports and analyses to identify potentially problematic data, make corrections, and eliminate root cause for data problems or justify solutions to be implemented by others

Create complex charts and databases, perform statistical analyses, and develop graphs and tables for publication and presentation

Serve as a resource for non-routine inquiries such as requests for statistics or surveys

Test prototype software and participate in approval and release process for new software

Provide documentation based on audit and reporting criteria to investigators and research staff

Qualification

RNA-seq workflowsWGS data analysisATAC-seq processingPythonRGenomics workflow developmentMulti-omics data integrationCloudDatabase skillsGenome browser expertiseData governanceAnalytical skillsCross-functional collaborationWriting skillsInterpersonal skills

Required

Bachelor's degree and three years of relevant experience or combination of education and relevant experience. Experience in a quantitative discipline such as economics, finance, statistics or engineering

Substantial experience with MS Office and analytical programs

Excellent writing and analytical skills

Ability to prioritize workload

Preferred

Transcriptomics and Gene Expression Analysis: Comprehensive RNA-seq workflows including read alignment (STAR, HISAT2), quantification (Salmon, Kallisto), QC (FastQC, MultiQC, RSeQC, Picard), normalization and differential expression (DESeq2, edgeR, limma), and pathway enrichment. Isoform analysis (StringTie) and fusion detection (STAR-Fusion) are a plus

Advanced Genomics Data Analysis Expertise: Extensive experience with WGS data from raw FASTQ through variant calling, joint genotyping, annotation, cohort-level QC, and interpretation. Proficiency with BWA-MEM/BWA, GATK Best Practices (BQSR, HaplotypeCaller, joint calling, VQSR), DeepVariant, bcftools, and scalable VCF/BCF/CRAM handling

Structural and Copy Number Variation: Experience with SV/CNV calling and QC (e.g., Manta, Delly, LUMPY, CNVnator), sample-level QC (coverage, duplication, contamination via VerifyBamID/Peddy/Somalier), and cohort metrics (Ti/Tv, call rate, Hardy–Weinberg)

Epigenomics and Chromatin Accessibility Analysis: Expertise in ATAC-seq processing and analysis, including alignment (BWA/Bowtie2), Tn5 shifting, peak calling (MACS2), replicate concordance (IDR), QC metrics (FRiP, TSS enrichment, nucleosome signal), differential accessibility (DiffBind/DESeq2), footprinting (HINT-ATAC), motif enrichment (HOMER/MEME), and browser tracks (bigWig/bigBed for IGV/UCSC). Regulatory element annotation using ENCODE/Roadmap resources

Multi-omics Data Integration: Experience integrating WGS, ATAC-seq, and RNA-seq to identify regulatory relationships (eQTL/aseQTL/caQTL, colocalization), linking chromatin accessibility to gene expression and variant effects

Advanced Python and R for Genomics: Deep proficiency in Python and R/Bioconductor with strong statistical and reproducible analysis skills

Genomics Workflow Development: Proven experience designing, testing, and deploying complex workflows using Nextflow and/or WDL/Cromwell (or Snakemake) in cloud or HPC environments, with containerization (Docker, Singularity) and CI/CD for reproducibility

Specialized Cloud and Database Skills: Hands-on experience with GCP (Cloud Storage, BigQuery), and genomics platforms (Terra, AnVIL). SQL skills and experience designing schemas for omics metadata/results; familiarity with gnomAD, ClinVar, Ensembl/RefSeq, dbSNP, UCSC

Genome Browser and Visualization Expertise: Proficiency creating custom track hubs and sessions for IGV/UCSC; ability to produce publication-quality visualizations and interactive dashboards for large-scale genomics data

Software Engineering Best Practices: Version control (Git/GitHub), code review, issue tracking, semantic versioning, packaging (setuptools/Poetry), automated testing (pytest), and comprehensive documentation (Sphinx/MkDocs)

Data Governance and FAIR Principles: Demonstrated experience with data lineage, provenance, audit trails, and adherence to FAIR; secure handling of controlled-access human genomic data (HIPAA/IRB compliance, DUAs), and submissions to dbGaP/GEO/SRA

Cross-functional Collaboration and Communication: Proven ability to work with wet-lab scientists, clinicians, and data engineers to translate biological questions into robust, actionable computational analyses