The Bioinformatics Analyst will play a critical role in both clinical operations and research, offering technical support and bioinformatics expertise to clinical genomics scientists, designing and maintaining analysis pipelines, and contributing to large-scale data analysis projects. The position also involves staying current with advancements in the field, applying AI and deep learning techniques, and conducting training sessions for researchers.

1. Analyze and interpret complex clinical and research data across various -omics data types, including genomics, transcriptomics, and epigenetics.

2. Apply strong conceptual and practical knowledge in analyzing WES, WGS, targeted NGS panels, and OGM datasets.

3. Collaborate with clinical genomics scientists to design informatics experiments and interpret complex biological data.

4. Develop, document, and maintain tailored algorithms and analysis methods for NGS data.

5. Contribute to the scale-out of bioinformatics infrastructure and participate in external scientific discussions on technology evaluation.

6. Stay updated with advancements in bioinformatics by attending conferences, workshops, and engaging with the scientific community.

7. Design, develop, and maintain bioinformatics pipelines, ensuring public documentation and example workflows are up-to-date.

8. Apply rigorous statistical analysis and predictive analytics to extract insights from datasets, including those curated from public domains.

9. Innovate through the implementation of AI and deep learning algorithms to generate new insights from large datasets.

10. Conduct training sessions and create comprehensive tutorials to educate researchers on bioinformatics techniques, tools, and best practices.

11. Perform other duties as required.

1. Strong understanding of statistical methods and data visualization techniques.

2. Proficiency in programming languages such as Python, R, and Perl.

3. Experience in Linux environments, cloud computing systems, and the development of statistical methods and algorithms.

4. Expertise in building and maintaining large-scale bioinformatics analysis pipelines for NGS data.

5. Hands-on experience with bioinformatics analysis, including variant filtering/calling, quality control monitoring, and variant annotation.

6. Experience with remote data storage and management, such as Amazon Web Services.

7. Familiarity with genomics databases and resources like ClinGen, HGMD, ClinVar, COSMIC, and OncoKB.

8. Excellent communication skills for effective collaboration and training.

9. Ability to work independently and as part of a team in a dynamic clinical laboratory environment.

10. Proficiency in MS Office (Outlook, Excel, Word) or similar software is required.

11. Experience with SAS, SPSS, and R software is preferred.

Education and Experience

1. Education: Master?s degree in Bioinformatics, Computational Biology, or a closely related field is required; PhD is preferred.

2. Years of relevant experience: 2 years is required.

3. Years of experience supervising: None.