Variant Scientist

Overview:
The Variant Scientist will work under supervision of the Clinical Geneticist.

**Responsibilities**:

- Accurately classify germline and somatic variants for Whole Genome/Whole Exome Sequencing, and panel tests.
- Utilize a variety of in-house software tools to analyze clinical molecular data
- Read and interpret scientific literature and curate relevant findings in a clear, concise, and precise manner
- Summarize inherited and somatic genetic test results to generate high quality clinical reports
- Perform critical quality control functions for molecular reports that complies with quality management programs and standard operating procedures
- Support improvements for current assays and processes
- Curate variants, genes, and diseases for scientific and clinical relevance
- May train junior team members
- Develop SOP for variant interpretation; maintain tracking documents in relation to the variant-scoring process.
- Collaborate with the Bioinformatician/IT regarding technology needs for new gene tests.

Qualifications:

- MSc or PhD degree in Cancer Genetics, Human Genetics, or Biological Sciences
- Minimum of 2-3 years of related experience in Variant Interpretation.
- Ability to research variants of unknown significance using literature searches with detailed annotation and integration of different data types in a timely fashion.
- Ability to use and understand external databases and tools such as COSMIC, TCGA, Predictive Networks, ASIAN, SEBINI, CARRIE, and INSIGHT and prediction software such as polyphen, SIFT, Mutation Taster, ALAMUT, UCSC Genome Browser, Ensembl Genome Browser, NCBI BLAST.
- Experience with DNA sequence analysis, next-generation sequencing (NGS) technologies, use of online databas entire ecosystem of real-world evidence to deliver real-time, actionable

**Skills**:

- Excellent analytic skills.
- Excellent writing and communication skills, organization, and meticulousness.
- Understand experimental methods, data analysis methods and inferences that can be applied to both single-gene experimentation and high-throughput analyses.
- Understand large genomic datasets, and their computational analysis, integrating protein and genetic interaction data, regulatory data at the DNA, RNA, and protein levels, polymorphism and disease-associated sequence variation data, quantitative phenotypic data and drug-target interaction data.