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# Data Scientist - Onsite

- **Company:** ApTask
- **Location:** Cambridge, MA, United States
- **Salary:** $221,707.0
- **Contract:** Temporary contract
- **Skills:** Amazon Web Services, Bash Shell, Computational Biology, Data Integration, R (Programming Language), Identity and Access Management, Python (Programming Language), Machine Learning, Population Genetics, Spatial Data Infrastructures, S3 Bucket, Feature Engineering, Data Ingestion, Deep Learning, Model Validation, Electronic Medical Records, Containerization, Docker
- **Published:** September 18, 2026
- **Apply:** https://www.dice.com/job-detail/4ad80805-46c5-4451-87a4-9fcf7fbee7d7

## About the Role

* 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, and validation
 * 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
 * Excellent written and verbal communication skills

Desired skills:

 * 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)

## Description

* Data Ingestion: Query and harmonize external resources to acquire relevant genetic, genomic, and multi-omics datasets
 * Genetic/Genomic Data Analysis: Perform quality control (QC) and analysis of genetic/genomic data, including genotype imputation, variant calling and annotation
 * Statistical Genetics: Conduct genetic association analyses at scale, including GWAS/PheWAS, rare-variant burden tests, fine-mapping, colocalization, polygenic scores, and Mendelian randomization
 * QTL Analysis: Conduct QTL analysis to identify genetic loci associated with quantitative and molecular traits, including eQTL, sQTL, and pQTL mapping
 * 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
 * Multi-Omics Data Integration: Integrate genetic datasets with other omics layers, including transcriptomic, epigenomic, proteomic, and spatial data to provide comprehensive insights into gene function and disease biology
 * Documentation and Reproducibility: Prepare detailed documentation of analysis methods and results, and deliver version-controlled, reproducible analysis workflows


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