Key Responsibilities: 

·         Conduct Mendelian Randomization analyses to infer causal relationships between genetic variants and disease risk.

·         Utilize and develop workflows using R for data analysis, including statistical modeling and visualization.

·         Work with large-scale genomic datasets, including genome-wide association studies (GWAS), whole-genome sequencing (WGS), and polygenic risk scores (PRS).

·         Perform survival analyses to examine genetic and epidemiological predictors of disease outcomes.

·         Utilize DNAnexus or other cloud-based platforms for genomic data processing, storage, and computational workflows.

·         Collaborate with multidisciplinary teams, including biostatisticians, clinicians, and geneticists, to interpret findings and drive translational insights.

·         Publish findings in peer-reviewed journals and present at scientific conferences.

Job Requirements:

·         A PhD (or equivalent) in Genetic Epidemiology, Bioinformatics, Biostatistics, Computational Biology, or a related field. Strong proficiency in Mendelian Randomization methods, including familiarity with MR-Base or other relevant tools.

·         Expertise in R programming and experience with statistical packages (e.g., TwoSampleMR, survival, tidyverse, Bioconductor).

·         Experience with handling and analyzing large genomic datasets, particularly GWAS, WGS, and PRS data.

·         Experience with multi-ancestry genomic studies or integrating different omics datasets.

·         Knowledge of genome-wide imputation methods and quality control procedures for genomic data.

·         Exposure to machine learning approaches in genomics.

·         Knowledge of survival analysis techniques (e.g., Kaplan-Meier, Cox proportional hazards models).

·         Familiarity with DNAnexus or other cloud-based genomic analysis platforms is highly desirable.

·         Strong analytical, problem-solving, and data visualization skills.

·         Ability to work independently and collaboratively in a multidisciplinary research environment.

·         Excellent written and verbal communication skills, with a track record of scientific publications.