Bioinformatics Scientist - III (Senior)

The Precision Genetics group within the Data and Genome Sciences Department is seeking a highly experienced Bioinformatics Scientist (Senior Level) to join our Computational Precision Genetics team . This role is ideal for a Ph.D.-level scientist with deep expertise in genetic and genomic data analysis, high-performance computing, and cloud technologies. You will play a critical role in advancing our understanding of complex diseases through computational genetics and integrated omics research.

• Data Acquisition: Query and acquire genetic/genomic datasets from public databases such as dbSNP, 1000 Genomes Project, gnomAD, GTEx, Ensembl, Open Targets, and ClinVar.

• Genomic Analysis: Execute QC and advanced analysis of large-scale genetic data, including genotype imputation, variant calling, and annotation using tools such as IMPUTE, Minimac, Eagle, BEAGLE, GATK, bcftools, samtools, and ANNOVAR.

• QTL Analysis: Identify quantitative trait loci using software like PLINK, R/qtl, or TASSEL.

• Population Genetics: Conduct studies on allele frequency, linkage disequilibrium, and population structure.

• Omics Integration: Merge genetic data with transcriptomic, proteomic, and epigenomic data to generate biological insights.

• Documentation: Maintain comprehensive documentation of analysis pipelines, methodologies, and outcomes for internal review and publication.

• Ph.D. in Genetics, Genomics, Computational Biology, Bioinformatics, or a related field.

• 5+ years of experience conducting advanced genetic data analysis.

• In-depth knowledge of population genetics, variant analysis, and genomic annotation.

• Proficiency in R, Python, and Bash , with an emphasis on reproducible scientific computing practices.

• Hands-on experience with HPC environments and AWS Cloud services (e.g., IAM, S3).

• Strong organizational, analytical, and communication skills.

• Proven ability to manage multiple complex objectives in a fast-paced, research-driven environment.

• Experience processing and analyzing real-world genetic datasets .

• Advanced expertise in AI/ML techniques applied to biological data.

• Deep understanding of statistical genetics principles and methods .

• Familiarity with genomic databases and ontologies.