RESPONSIBILITIES

•  Perform quality analysis of sequence data and assist others in interpretation of analyzed data.
•  Define quality metrics for assessing library and sequencer performance.
•  Design, Develop and improve tools to evaluate quality of data generated by various sequencing platforms and library types.
•  Refine and improve existing analytical tools and provide high quality data to collaborators.
•  Provide clear and concise professional reports.
•  Work closely with scientists and production staff to improve production automation quality.
•  Apply analytical skills and creativity to troubleshoot problems of a moderate scope.
•  Present technical and analytical status in meetings.

•  Stay current with new sequencing technologies.
•  Provide feedback on the improvement of the library construction and sequencing procedures.
•  Train biologists to use the available data analysis tools.

REQUIREMENTS

•  B.S. in Computer Engineering, Bioinformatics or a related field with a minimum of two years related experience in bioinformatics, or an equivalent combination of education and experience. MS degree is preferred.
•  Strong background in bioinformatics, mathematics, molecular biology and genomics.
•  Demonstrated experience applying statistics to analyze large biological datasets.
•  Proficiency with programming languages including PERL, Python, Java or others in a Unix environment.
•  Strong problem-solving, decision-making, and analytical skills to make sound judgments and recommend creative solutions to moderately complex problems.
•  Ability to effectively learn and apply new technology concepts.
•  Effective time management, organization, and planning skills.
•  Talent for working well under short deadlines and handling multiple tasks simultaneously.
•  Ability to work effectively in a diverse team environment.

•  Knowledge and experience with laboratory DNA sequencing technologies and genetics or related field or equivalent experience preferred.
•  Some experience with database structure and management.
•  Experience applying machine learning, pattern recognition or modeling to scientific problems.
•  Experience in a multi-disciplinary scientific or production environment.

TERMS

HOW TO APPLY