Setting comprising individuals of diverse talents across computer science, molecular biology, statistics and genomics. He/she will be part of a dynamic bioinformatics team, advancing the state of art in genomic data analysis with multiple projects available to apply skill sets such as data science (data aggregation and data querying with database tools), metadata analyses, statistical modeling, and software automation. These techniques will be applied to building new clinical assays, matching patients to therapeutics, solving client-specific statistical problems and creation of new software tools. The applicant will also learn principles of software validation and testing, software versioning tools, software engineering and art of transitioning from research to production environments.
The training period would initially extend across the applicant’s scholastic semester, which would involve at minimum ½ time commitment but could extending during the school year into a part-time commitment.
• Application of one or more of these skill sets: data aggregation, data querying, statistical modeling, machine learning, pipeline automation for NGS datasets.
• Development of robust, reliable and efficient software.
• Communication with multiple peers to gather requirements and provide status updates to stakeholders.
• Demonstration of the software and/or results of the project
• Documentation on the how-to of the software and/or analyses of the project
REQUIRED KNOWLEDGE, SKILLS AND ABILITIES
• Understanding/course work on one or more: database tools, computer programming, statistical modeling, or machine learning.
• Familiarity working in Linux/UNIX environment, including shell scripting and bash (preferred).
• Proficiency in a programming language: SQL, Python, Perl, R, or SAS.
• Experience giving presentations of analysis results to non-technical audiences.
• Exposure to data analysis using next generation sequencing, multi-analytic instrumentation and other *omic data (preferred).
• Appropriate verbal and written communication skills to function within a professional work environment.
MINIMUM REQUIRED EDUCATION AND EXPERIENCE
• Enrollment in senior level classes with research (preferred) and relevant coursework from fields such as data science, computer science or bioinformatics.
• Applicant must provide examples of both programming code and data analysis presentation(s).