Computational Biologist - Biological Data Analytics

The Lawrence J. Ellison Institute for Transformative Medicine strives to leverage technology, spark innovation, and drive interdisciplinary evidence-based research to reimagine and redefine cancer treatment, enhance health, and transform lives. Under the visionary leadership of David B. Agus, MD, The Ellison Institute was designed to tackle the difficult questions in health care and research to push the boundaries of medicine forward. To accomplish this, the Institute offers multifaceted programs, including a cancer center, cross-disciplinary research laboratories, a health policy think-tank, and community outreach and educational opportunities. Please visit http://ellison.usc.edu to learn more about The Ellison Institute and our brand new, state-of-the-art facility in West Los Angeles.

Contribute to transforming the way we treat cancer as a member of the Ellison Institute for Transformative Medicine. Use your skills in data analytics and background in biology to uncover the answers in the chaos of biological big data.

We are seeking an enthusiastic Computational Biologist with an interest in cancer biology to complement our Quantitative Biology, Bioinformatics and Biostatistics support team. In this role, you will develop and implement robust and effective data management and analytical solutions to answer biological questions as part of a multi-disciplinary team of clinicians, experimentalists, computer scientists, engineers, statisticians, and biologists. You will collaboratively formulate and execute plans for data analysis, management, and visualization to produce rigorous, reproducible insights while ensuring the quality and integrity of the data throughout its lifecycle. These cancer-related data sets may include next-gen-sequencing data, high-dimensional multi-omic data, time series from live cell microscopy, and complex clinical data from electronic medical records.

Specific responsibilities:

• Develop and implement robust and effective analytical solutions to answer biological questions. Collaboratively formulate and execute plans for data processing, analysis, management, and visualization with principal investigators and scientists.
• Support lab researchers in their collection, processing and analysis of experimental, clinical and other biomedical data to ensure that it is properly stored both for investigation and for longer-term preservation; identify appropriate storage resources, and ensure that appropriate metadata are included.
• Work with other analysts and institute researchers and participate in the analysis of data, focusing on data quality assurance, cleaning, aggregation and integration (wrangling), and visualization.
• Test, run, and debug software and data processing pipelines.
• Keeps informed of developments in the field. Reads journals and other relevant publications, attends professional association meetings and seminars as appropriate.

Qualifications

Demonstrable Personal Attributes:

• Ability to thrive in a service-oriented, highly collaborative, multi-disciplinary environment.
• Strong organizational skills and attention to detail.
• A creative, experimental, and open mindset, eager to learn new things.
• Interest in technological advances, solutions.
• Strong collaborative spirit and ability to communicate to scientists and nonscientists.

Knowledge and Skills:

• Degree in biology or related fields.
• Ability to input, output, and manipulate data using the R statistical analysis platform, including familiarity with the tidyverse ecosystem and the rmarkdown package. Sample code required.
• Experience with Linux shell scripting, command line tools, version control (Git preferred), and simple system administration tasks. Familiarity with data management and analysis practices in biomedical research, including metadata and ontology usages and reproducible research concepts and practices.
• Good writing and verbal communication abilities
• Proficient with Microsoft Excel, Word, and PowerPoint.
• Preferred: Experience with the Bioconductor R package suite, and familiarity with RNA and DNA sequencing pipeline generation and implementation.

• Preferred: working knowledge of relational databases. Knowledge of other storage solutions and resources for scientific data (e.g. NoSQL databases, HDF5, …).

Education and Experience:

• Master's degree. Combined experience/education as substitute for minimum education.
• Two or more years' experience in data collection, analysis, transformation, management, and/or warehousing.

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