We are looking for a data scientist with extensive experience in genetic data analysis to contribute to our innovative research efforts. Key Responsibilities: • Data Ingestion: Query external databases to acquire relevant genetic/genomic datasets (e.g., dbSNP, 1000 Genomes Project, gnomAD, GTEx, Ensembl, Open Targets, ClinVar). • Genetic/Genomic Data Analysis: Perform quality control (QC) and analysis of genetic/genomic data, including genotype imputation from array data, variant calling and annotation using state-of-the-art methods (e.g., IMPUTE, Minimac, Eagle, BEAGLE, GATK, bcftools, samtools, ANNOVAR). • QTL Analysis: Conduct QTL analysis to identify genetic loci associated with quantitative traits, utilizing tools such as PLINK, R/qtl, or TASSEL. • Population Genetics Analysis: Analyze genetic variation across populations, including allele frequency estimation, linkage disequilibrium, and population structure analysis. • Data Integration: Integrate genetic datasets with other omics data, including genomic, epigenomic, transcriptomic and proteomic data, to provide comprehensive insights into gene function and regulation. • Documentation: Prepare detailed documentation of analysis methods and results in a timely manner. Quals-- Required Qualifications, skills and experience:- • Minimum: Ph.D. in Genetics, Genomics, Computational Biology, or a related field. • A proven track record of over 5 years in genetic data analysis. • Fundamental understanding of statistical methods and genetic data analysis and integration (e.g., variant analysis, population genetics, genomic annotations). • 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 and adapting to changing priorities. • Excellent written and verbal communication skills. Preferred Qualifications: • Experience with real-world genetic data processing and analysis. • Proficient in genetic/genomic data analysis tools and techniques. • Understanding of statistical genetics principles and methods. • Expertise in AI/ML. Key skills:- • Proficient in genetic/genomic data analysis tools and techniques-e.g., variant analysis, population genetics, genomic annotations. • Proficiency in R, Python, and Bash. • High-performance computing (HPC) systems and AWS Cloud Computing (e.g., IAM, S3 buckets).