Job Description

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.

Equal Opportunity Employer / Disabled / Protected Veterans

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For temporary assignments lasting 13 weeks or longer, AllSTEM Connections is pleased to offer major medical, dental, vision, 401k and any statutory sick pay where required.

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We also consider for employment qualified applicants regardless of criminal histories, consistent with legal requirements, including, if applicable, the City of Los Angeles' Fair Chance Initiative for Hiring Ordinance. Pursuant to applicable state and municipal Fair Chance Laws and Ordinances, we will consider for employment-qualified applicants with arrest and conviction records, including, if applicable, the San Francisco Fair Chance Ordinance. For Los Angeles, CA applicants: Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.

Additional Skills

(none specified)

AllSTEM Representative Contact Info

Account Executive:

Broughton

Branch Phone:

[click to reveal phone number](909) 244-1777

Location:

Ontario, CA

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