The newly formed Cancer Data Sciences group at the UCLA David Geffen School of Medicine and UCLA Jonsson Cancer Centre is seeking a Junior Bioinformatician with pipeline and data science experience that will help us perform research that will transform the lives of cancer patients. You will be working with a broad team of quantitative analysts, including Statisticians, Data Scientists, Clinical and Basic trainees, and other bioinformaticians. In this role, you will develop new quantitative strategies to improve our understanding and ability to treat cancer and apply your knowledge of biology and of methods for molecular, cellular or radiologic data-analysis to uncover general principles of cancer, and use these to create clinically useful biomarkers. In addition, you will develop, extend and apply computational pipelines for data-analysis to patient cohorts ranging from tens of individuals to hundreds of thousands that will be executed at scale via distributed software solutions on both local HPC and cloud-based assets. Our datasets comprise petabytes, and are growing rapidly, linked to key clinical endpoints.
UCLA is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected Veteran status.
-Working knowledge of R, Perl or Python programming
-Understanding of molecular biology
-Basic univariate and probabilistic statistical understanding
-Knowledge of LINUX/Unix operating system, and source-code versioning systems
-Applied experience with NGS, proteomics or radiomic data (>1 year)
-Experience with applied machine-learning (e.g. hyperparameter optimization)
-Working knowledge of containerization (e.g. Docker, Singularity)
-Computer science knowledge, including software design patterns
-Knowledge of relational database software (e.g. Oracle, Postgres)
-BSc or MSc Training in Computational Biology, Computer Science, Biostatistics or a related discipline preferred