Bioinformatics Scientist Position Available In Essex, New Jersey

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., db

SNP, 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.