Job Description

Must Have Requirements

• PhD in Genetics, Bioinformatics, Computational Biology, Biostatistics, or a related quantitative field
• Minimum 5+ years of post-PhD experience in human or complex disease genetics (mandatory)
• This is a fully onsite role in Cambridge, MA ; no remote or hybrid work allowed
• Candidates must have strong hands-on experience in multi-omics data integration , including:
• Bulk RNA-seq
• Single-cell RNA-seq (required)
• Genotype/GWAS data analysis
• Spatial transcriptomics (preferred)
• Proteomics data (e.g., Olink preferred)
• Strong experience in statistical genetics and transcriptomics analysis
• Proficiency in R, Python, and Bash , with ability to develop reproducible and scalable workflows
• Experience working with high-performance computing (HPC) systems
• Hands-on experience with AWS cloud platforms (e.g., S3, IAM, and related services)
• Experience working with large-scale external genomic datasets
• Must have a strong publication record (to be included in resume)
• Must be willing to relocate at own expense if non-local and join as per project timelines

Key Responsibilities

• Conduct large-scale statistical genetics analyses to support target discovery and validation
• Perform genome-wide association studies (GWAS) using population-scale biobank datasets (e.g., UK Biobank, FinnGen, Our Future Health, and other consortium datasets)
• Develop, implement, and optimize scalable and reproducible computational pipelines for genomic data analysis
• Perform meta-analysis of genetic association datasets and integrate public and proprietary summary statistics
• Execute post-GWAS analyses including fine-mapping, colocalization, Mendelian Randomization, transcriptome-wide association studies (TWAS), and polygenic risk scoring (PRS)
• Integrate and analyze multi-omics datasets such as bulk RNA-seq, single-cell RNA-seq, ATAC-seq, QTLs, and proteomics data for gene and target prioritization
• Contribute to data integration strategies linking genetics with functional genomics and molecular phenotypes
• Work closely with cross-functional teams including computational scientists, disease area experts, and wet-lab biologists
• Stay current with emerging methodologies in statistical genetics, computational biology, and AI/ML applications in genomics
• Contribute to the interpretation and communication of complex genetic findings to support drug discovery programs

Required Qualifications

• PhD (or equivalent) in Genetics, Bioinformatics, Computational Biology, Statistical Genetics, Biostatistics, Genetic Epidemiology, or related quantitative field
• Minimum 5+ years of post-PhD experience in human genetics or complex disease genetics research
• Strong hands-on experience in GWAS and large-scale genomic data analysis
• Demonstrated experience in multi-omics data integration, including RNA-seq and/or single-cell RNA-seq
• Strong programming skills in R, Python, and Bash
• Experience working in HPC environments and cloud platforms (AWS including S3, IAM, etc.)
• Strong understanding of reproducible research practices and scalable pipeline development
• Experience working with large, complex datasets in collaborative research environments
• Excellent communication skills and ability to work in multidisciplinary teams

Preferred Qualifications

• Experience with spatial transcriptomics and emerging single-cell technologies
• Proteomics experience (e.g., Olink or similar platforms)
• Familiarity with machine learning/AI approaches applied to genomics or multi-omics data
• Experience in cardiometabolic disease, immunology, neuroscience, or other complex disease areas
• Strong knowledge of advanced statistical genetics methods including TWAS, Mendelian Randomization, colocalization, fine-mapping, and PRS modeling

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.

We are committed to working with and providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation for any part of the employment process, please contact your staffing representative who will reach out to our HR team.

AllSTEM Connections participates in the E-Verify program in certain locations as required by law. Learn more about the E-Verify program.

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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