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DataTecnica · 4 days ago

Statistical Genetics Scientist

United States

Full-time

Remote

Mid, Senior Level

$100K/yr - $200K/yr

DataTecnica is a biomedical AI company specializing in analytics for neurological and other diseases. They are seeking a Statistical Genetics Scientist to assist with analysis for precision medicine programs targeting Parkinson’s disease risk and progression for GP2, driving large GWAS efforts integrating -omic data at biobank scale.

Artificial Intelligence (AI)BiopharmaBiotechnologyCloud Data ServicesGeneticsHealth CareInformation TechnologyMachine LearningNeuroscienceSoftware

Responsibilities

Design and execute large-scale GWAS for risk and progression phenotypes related to PD and related disorders

Lead meta-analyses across heterogeneous cohorts with harmonized phenotypes

Conduct statistical fine-mapping and credible set analysis at implicated loci

Execute colocalization analyses (coloc, eCAVIAR) to identify shared causal variants across traits

Run SMR/GSMR analyses integrating brain eQTL and pQTL data to prioritize causal genes

Perform genetic correlation analyses (LDSC, HDL) between related phenotypes

Build and deploy polygenic risk scores

Design and execute bidirectional Mendelian Randomization analyses and MR sensitivity analyses

Integrate eQTL/pQTL data from brain tissues and relevant cell types

Collaborate with bioinformatics team on multi-omic follow-up of GWAS signals and exploratory multi-omics

Support network analyses by providing robustly prioritized gene lists

Qualification

GWAS pipelinesMendelian RandomizationStatistical analysisBiobank-scale datasetsRPythonCloud-based genomicsStatistical foundationEpidemiology best practicesNeurodegenerative disease geneticsLongitudinal phenotype analysisMulti-ancestry GWASEQTL/pQTL resources

Required

MS or PhD in Statistical Genetics, Human Genetics, Genetic Epidemiology, Biostatistics, Bioinformatics or related quantitative field

Published experience conducting and publishing GWAS (first-author publications demonstrating methodological rigor)

Expert-level proficiency in GWAS pipelines (PLINK2, REGENIE, BOLT-LMM)

Strong command of Mendelian Randomization methods and software (TwoSampleMR, MR-Base, MendelianRandomization R package)

Experience with summary statistics-based methods (LD score regression, genetic correlation, colocalization)

Proficiency in R and/or Python for statistical analysis and visualization

Hands-on experience with cloud-based genomics platforms (e.g., Terra, Verily Workbench, UK Biobank Research Analysis Platform, DNAnexus, or equivalent)

Strong statistical foundation (regression, mixed models, multiple testing, causal inference)

Track record of working with biobank-scale datasets (100K+ samples)

General best practices in epidemiology