Senior Bioinformatics Scientist - III

AllSTEM Connections; Cambridge (MA); On-site; USD 198,000 - 242,000; Full time

Benefits offered by this job

Benefits

Job summary

AllSTEM Connections in Cambridge, MA seeks a Senior Bioinformatics Scientist - III for a 24-month contract. You will apply ML to multi-omics data, support target identification, patient stratification, and biomarker discovery in precision genetics.

The role demands a PhD in a relevant field, 5+ years of genetic data analysis, and proven HPC/AWS experience, with strong collaboration and communication skills in a fast‑paced environment.

Qualifications

• PhD in Genetics, Genomics, Statistical Genetics, Computational Biology, or related field.
• 5+ years of experience in genetic data analysis.
• Experience applying machine learning to high-dimensional biological data.
• Hands-on experience with multi-omics data integration.
• Proficiency in R, Python, and Bash; HPC and AWS experience.
• Strong communication and collaboration skills.

Responsibilities

• Ingest, harmonize, and analyze genetic, genomic, and multi-omics datasets.
• Perform QC and variant calling/annotation with state-of-the-art tools.
• Conduct large-scale genetic association analyses (GWAS/PheWAS).
• Develop ML models for biomarker discovery and patient stratification.
• Integrate multi-omics data to derive biological insights.
• Document analyses with reproducible pipelines.

Job description

Title: Senior Bioinformatics Scientist - III

Pay Rate: $106/ HR. with Benefits

Location: Cambridge, MA

Duration – 24 Months +

Shift Time - Standard Shift

Job Description:

Location:

Department:

Group:

The Precision Genetics group within the Data and Genome Sciences Department is seeking a skilled Contractor to join our Computational Precision Genetics team. We are looking for a data scientist who combines deep expertise in human genetic data analysis with strong machine learning capabilities and hands‑on experience integrating multi‑omics data, to support our target identification, patient stratification, and biomarker discovery efforts.

Required Qualifications:

• Ph.D. in Genetics, Genomics, Statistical Genetics, Computational Biology, or a related field.
• A proven track record of over 5 years in genetic data analysis.
• Strong understanding of statistical methods and genetic data analysis and integration (e.g., variant analysis, GWAS and QTL mapping, population genetics, genomic annotations).
• Demonstrated experience applying machine learning to high‑dimensional biological data, including feature engineering, model selection, validation, and avoidance of overfitting and confounding.
• Hands‑on experience integrating multi‑omics data (e.g., transcriptomics, proteomics, epigenomics) with genetic data.
• Proficiency in R, Python, and Bash, with the ability to establish best practices for reproducible data analyses.
• Experience with high‑performance computing (HPC) systems and AWS Cloud Computing (e.g., IAM, S3 buckets).
• A collaborative and self‑motivated individual with a strong work ethic, capable of managing multiple objectives in a dynamic environment and adapting to changing priorities.
• Excellent written and verbal communication skills.

Preferred Qualifications:

• Experience with real‑world and large‑scale biobank genetic data (e.g., UK Biobank, All of Us, FinnGen, electronic health record‑linked cohorts).
• Experience with deep learning approaches for genomics, including sequence‑based and variant‑effect prediction models.
• Familiarity with single‑cell and spatial transcriptomics analysis.
• Experience supporting drug target identification and validation, or biomarker discovery in a pharmaceutical or biotechnology setting.
• Familiarity with workflow managers (e.g., Nextflow, Snakemake) and containerization (e.g., Docker, Singularity).

Note:

• Onsite role at Cambridge, MA.
• Do not submit candidates who are looking for remote.
• Do not submit candidates with just BS/MS.
• Proficient in human genetic/human genomic data analysis tools and techniques-e.g., variant analysis, population genetics, genomic annotations.
• Multi‑Omics Data Integration
• Proficiency in R, Python, and Bash.
• High‑performance computing (HPC) systems and AWS Cloud Computing (e.g., IAM, S3 buckets).

Key Responsibilities:

• Data Ingestion: Query and harmonize external resources to acquire relevant genetic, genomic, and multi‑omics datasets (e.g., dbSNP, 1000 Genomes Project, gnomAD, GTEx, Ensembl, Open Targets, ClinVar, GWAS Catalog, UK Biobank, Gene Expression Omnibus).
• Genetic/Genomic Data Analysis: Perform quality control (QC) and analysis of genetic/genomic data, including genotype imputation from array data, variant calling and annotation using state‑of‑the‑art methods (e.g., IMPUTE, Minimac, Eagle, BEAGLE, GATK, bcftools, samtools, ANNOVAR, VEP).
• Statistical Genetics: Conduct genetic association analyses at scale, including GWAS/PheWAS, rare‑variant burden and collapsing tests, fine‑mapping, colocalization, polygenic scores, and Mendelian randomization (e.g., PLINK, REGENIE, SAIGE, GCTA, SuSiE, coloc, LDSC).
• QTL Analysis: Conduct QTL analysis to identify genetic loci associated with quantitative and molecular traits, including eQTL, sQTL, and pQTL mapping, utilizing tools such as tensorQTL, FastQTL, PLINK, or R/qtl.
• Population Genetics Analysis: Analyze genetic variation across populations, including allele frequency estimation, linkage disequilibrium, relatedness, and ancestry/population structure analysis.
• Machine Learning: Develop, benchmark, and validate machine learning models on high‑dimensional genetic and molecular data for tasks such as variant effect prediction, patient stratification, and biomarker or treatment‑response prediction; apply rigorous cross‑validation, control for batch and ancestry confounding, and use interpretability methods to translate models into testable biological hypotheses (e.g., scikit‑learn, XGBoost, PyTorch, SHAP).
• Multi‑Omics Data Integration: Integrate genetic datasets with other omics layers, including transcriptomic (bulk and single‑cell RNA‑seq), epigenomic, proteomic (e.g., OLINK, mass spectrometry), and spatial data, to provide comprehensive insights into gene function and disease biology (e.g., DESeq2, limma, Seurat, scanpy).
• Documentation and Reproducibility: Prepare detailed documentation of analysis methods and results in a timely manner, and deliver version‑controlled, reproducible analysis workflows (e.g., Git, Nextflow, Snakemake).