Hybrid Genomics Scientist: AI-Powered Target Discovery

Genomics; North Carolina; Hybrid; USD 110,000 - 160,000; Full time

Benefits offered by this job

PTO: 25 days vacation + 8 holidays

401k

Health/Dental/Vision

HSA

Life/AD&D & Disability

Hybrid working

Life/AD&D

Disability coverage

Job summary

Genomics in RTP/NC is seeking an early-career Scientist in the Life Sciences team to develop and validate therapeutic targets, and to advance patient stratification using polygenic risk scores and diverse ‘omic data. You will work with statistics, genetics, and AI/ML methods to generate robust insights and communicate them to stakeholders.

The role emphasizes collaboration, rigorous methodological development, and contribution to translational efforts across development programs, with a hybrid

Qualifications

• Statistics (modelling, regression)
• Genetics and molecular biology
• Statistical programming with Python or R
• GWAS, colocalization, fine-mapping, Mendelian randomization, polygenic risk scores
• Phenotyping from electronic health records
• Bayesian inference, high-dimensional statistics, causal inference
• Software engineering practices: version control, testing, documentation
• Pipelines with WDL, Snakemake or NextFlow
• Cloud computing and trusted research environments (DNAnexus/Verily Workbench)
• Statistical genetics methods development

Responsibilities

• Generate and test therapeutic hypotheses using in-house data and genetic evidence
• Develop patient stratification strategies using genetic and clinical data
• Communicate findings to internal and external stakeholders
• Develop and execute rigorous statistical and computational approaches

Skills

Statistics & modelling

Genetics & molecular biology

Statistical programming (Python or R)

Cloud computing

Tools

Git & GitHub

Snakemake

NextFlow

WDL

DNAnexus/Verily Workbench

Verily Workbench

Job description

Genomics in RTP/NC is seeking an early-career Scientist in the Life Sciences team to develop and validate therapeutic targets, and to advance patient stratification using polygenic risk scores and diverse ‘omic data. You will work with statistics, genetics, and AI/ML methods to generate robust insights and communicate them to stakeholders.

The role emphasizes collaboration, rigorous methodological development, and contribution to translational efforts across development programs, with a hybrid