Senior Bioinformatics Scientist - III
Computational Precision Genetics Contractor
Location: Cambridge, MA - Onsite
Department: Data and Genome Sciences
Group: Precision Genetics
Position Overview
The Precision Genetics group within the Data and Genome Sciences Department is seeking a skilled contractor to join its Computational Precision Genetics team.
This role is ideal for a data scientist with deep expertise in human genetic and genomic data analysis, strong machine learning capabilities, and hands-on experience integrating multi-omics datasets. The position will support target identification, patient stratification, and biomarker discovery initiatives.
Required Qualifications
- Ph.D. in Genetics, Genomics, Statistical Genetics, Computational Biology, or a related field.
- 5+ years of experience in genetic data analysis.
- Strong understanding of statistical methods and genetic data analysis and integration, including:
- Variant analysis
- GWAS
- QTL mapping
- Population genetics
- Genomic annotations
- Demonstrated experience applying machine learning to high-dimensional biological data, including feature engineering, model selection, validation, and approaches to minimize overfitting and confounding.
- Hands-on experience integrating multi-omics data, including transcriptomics, proteomics, and epigenomics, with genetic data.
- Proficiency in R, Python, and Bash.
- Experience establishing reproducible data analysis practices.
- Experience with high-performance computing environments.
- Experience with AWS cloud infrastructure, including IAM and S3.
- Strong collaboration skills and the ability to work independently in a dynamic research environment.
- Ability to manage multiple objectives and adapt to changing priorities.
- Excellent written and verbal communication skills.
Preferred Qualifications
- Experience working with real-world and large-scale biobank genetic datasets, such as UK Biobank, All of Us, FinnGen, or electronic health record-linked cohorts.
- Experience applying deep learning approaches to genomics, including sequence-based and variant-effect prediction models.
- Familiarity with single-cell and spatial transcriptomics analysis.
- Experience supporting drug target identification, target validation, or biomarker discovery within a pharmaceutical or biotechnology environment.
- Familiarity with workflow management tools such as Nextflow or Snakemake.
- Experience with containerization technologies such as Docker or Singularity.
Key Requirements
- This is an onsite position in Cambridge, Massachusetts.
- Candidates seeking fully remote work should not be considered.
- A Ph.D. is required; candidates whose highest degree is a BS or MS should not be considered.
Key Skills
- Human genetic and genomic data analysis
- Variant analysis
- Population genetics
- Genomic annotation
- Statistical genetics
- Machine learning
- Multi-omics data integration
- R
- Python
- Bash
- High-performance computing
- AWS cloud computing
Key Responsibilities
Data Ingestion
- Query, acquire, and harmonize external genetic, genomic, and multi-omics datasets.
- Work with resources such as dbSNP, the 1000 Genomes Project, gnomAD, GTEx, Ensembl, Open Targets, ClinVar, GWAS Catalog, UK Biobank, and Gene Expression Omnibus.
Genetic and Genomic Data Analysis
- Perform quality control and analysis of genetic and genomic datasets.
- Conduct genotype imputation from array data.
- Perform variant calling and genomic annotation using current methods and tools.
- Work with platforms and tools such as IMPUTE, Minimac, Eagle, BEAGLE, GATK, bcftools, samtools, ANNOVAR, and VEP.
Statistical Genetics
- Conduct large-scale genetic association analyses, including:
- GWAS and PheWAS
- Rare-variant burden and collapsing tests
- Fine-mapping
- Colocalization
- Polygenic scoring
- Mendelian randomization
- Utilize statistical genetics tools such as PLINK, REGENIE, SAIGE, GCTA, SuSiE, coloc, and LDSC.
QTL Analysis
- Conduct quantitative trait locus analyses to identify genetic loci associated with molecular and quantitative traits.
- Support eQTL, sQTL, and pQTL mapping.
- Utilize tools such as tensorQTL, FastQTL, PLINK, and R/qtl.
Population Genetics
- Analyze genetic variation across populations.
- Perform allele frequency estimation, linkage disequilibrium analysis, relatedness analysis, and ancestry/population structure analysis.
Machine Learning
- Develop, benchmark, and validate machine learning models using high-dimensional genetic and molecular datasets.
- Apply models to areas such as:
- Variant-effect prediction
- Patient stratification
- Biomarker prediction
- Treatment-response prediction
- Apply rigorous cross-validation techniques.
- Control for batch effects, ancestry-related confounding, and other potential sources of bias.
- Apply interpretability methods to translate model outputs into testable biological hypotheses.
- Utilize tools such as scikit-learn, XGBoost, PyTorch, and SHAP.
Multi-Omics Data Integration
- Integrate genetic datasets with multiple omics layers, including:
- Bulk and single-cell transcriptomics
- Epigenomics
- Proteomics
- Spatial omics data
- Apply integrated analyses to generate insights into gene function and disease biology.
- Utilize tools such as DESeq2, limma, Seurat, and scanpy.
Documentation and Reproducibility
- Prepare detailed and timely documentation of analytical methods, workflows, and results.
- Develop version-controlled and reproducible computational analysis workflows.
- Utilize technologies such as Git, Nextflow, and Snakemake.
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Additional Skills
(none specified)
AllSTEM Representative Contact Info
Account Executive:
Broughton
Branch Phone:
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Location:
Ontario, CA