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Bioinformatics Scientist

Job ID: 25-09136
Job Title:             Bioinformatics Scientist
Duration:            23 months, 40 hrs / week
Location:             Cambridge, MA 02141

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).

Responsibilities:

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.
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