Data Scientist – Onsite

ApTask Global Workforce; Cambridge (MA); On-site; USD 124,000 - 152,000; Full time

Benefits offered by this job

Medical

Dental

Vision

Sick Pay

Job summary

Aptask Global Workforce is seeking a Data Scientist onsite for a Pharmaceutical Company in Cambridge, MA. The role centers on integrating multi‑omics data, applying machine learning models, and supporting target identification, patient stratification, and biomarker discovery within the Precision Genetics group.

The candidate will perform QC and analysis of genetic data, GWAS/QTL analyses, and develop scalable workflows using R, Python, Bash, and HPC on AWS.

Qualifications

• PhD in Genetics, Genomics, Statistical Genetics, Computational Biology or related field.
• 5+ years of genetic data analysis experience.
• Experience applying ML to high‑dimensional biological data.
• Hands‑on multi‑omics data integration with genetics data.
• Proficiency in R, Python and Bash; reproducible analyses.

Responsibilities

• Data ingestion and harmonization of genetic, genomic and multi‑omics datasets.
• QC and analysis of genetic/genomic data including imputation and variant calling.
• GWAS/PheWAS, QTL mapping, population genetics and Mendelian randomization analyses.
• Develop ML models for variant effect prediction and patient stratification.
• Integrate multi‑omics data and document reproducible workflows.

Skills

R

Python

Bash

Deep learning

Communication skills

Education

Ph.D. in Genetics or related field

Tools

AWS

Nextflow

Snakemake

Docker

Singularity

HPC

Job description

Aptask Global Workforce (AGW) is seeking a Data Scientist – Onsite for a position with a Pharmaceutical Company located in Cambridge, MA. This is a 23+ month contract opportunity.

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. This role supports target identification, patient stratification, and biomarker discovery efforts within the Precision Genetics group.

Responsibilities:

• 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

Requirements:

• 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)

Pay range: up to $106.59 per hour

Only candidates available and ready to work directly as Aptask Global Workforce (AGW) employees will be considered for this position.

Benefits of working with ApTask Global Workforce include:

• Medical
• Dental
• Vision
• Sick Pay (for applicable states/municipalities)

ApTask Global Workforce is an Equal Opportunity Employer. Candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability or status as a protected veteran.