Bioinformatics Scientist - III (Senior)

TALENT Software Services; Cambridge (MA); On-site; USD 120,000 - 160,000; Full time

Job summary

TALENT Software Services is hiring a Contractor in Cambridge, MA for a Computational Precision Genetics team. You will apply ML to genetic and multi-omics data to support target identification, patient stratification, and biomarker discovery.

Strong programming in R, Python, and Bash is required, with HPC/AWS experience. The role focuses on integrating multi-omics, performing GWAS/QTL analyses, and delivering reproducible analyses in a collaborative environment.

Qualifications

• PhD in Genetics, Genomics, Statistical Genetics, Computational Biology, or related field.
• 5+ years of experience in genetic data analysis.
• Strong understanding of GWAS, QTL mapping, population genetics and genomic annotations.
• Experience applying ML to high‑dimensional biological data and avoiding confounding.

Responsibilities

• Data Ingestion: query and harmonize external resources for genetic and multi‑omics datasets.
• Genetic/Genomic Data Analysis: QC and analysis, variant calling and annotation.
• Statistical Genetics: GWAS/PheWAS, rare-variant tests, fine‑mapping, colocalization, polygenic scores, MR.
• QTL Analysis: identify loci for eQTL, sQTL, pQTL using tensorQTL, FastQTL, PLINK.
• Population Genetics Analysis: allele frequency, LD, ancestry structure.
• Machine Learning: develop/validate models for variant prediction and patient stratification.
• Multi‑Omics Data Integration: integrate transcriptomics, epigenomics, proteomics with genomic data.
• Documentation and Reproducibility: provide reproducible workflows (Git, Nextflow, Snakemake).

Skills

Genetic data analysis

Machine learning

Multi-omics data integration

R

Python

Bash

HPC

AWS

Communication

Education

PhD in Genetics/Genomics/Statistical Genetics

Tools

Nextflow

Snakemake

Docker

Singularity

GATK

PLINK

Seurat

Job description

Location: Cambridge, MA, Onsite.

Department: Data and Genome Sciences

Group: Precision Genetics

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.

Key skills

• Proficient in human genetic/human genomic data analysis tools and techniques-e.g., variant analysis, population genetics, genomic annotations.
• Machine Learning
• 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).