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Bioinformatician 50% FTE

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POST DATE 9/2/2016
END DATE 11/2/2016

Stanford University Stanford, CA

Stanford, CA
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Stanford University

Bioinformatician 50% FTE

Job Number: 72348

Stanford Department of Biochemistry, a premier research-intensive intuition is actively recruiting for a dynamic candidate to serve as a bioinformatician ( 50% FTE) in the Julia Salzman lab. The Salzman lab uses experimental and statistical tools to construct a high dimensional picture of gene regulation, including cis and trans control of the full repertoire of RNAs expressed by cells. For more information, please visit the lab website here: ( bioinformatician will work in a highly collaborative environment, carry out data analysis and integration, and apply best-in-class algorithms - or develop new algorithms - that directly address the motivating biological questions. Further, the bioinformatician will develop reproducible and well-documented analytical pipelines for the analysis of high-throughput data, such as single-cell RNA-seq. Proficiency in high-throughput genomic data analysis and a good understanding of biological systems are required, as are experience in programming and a solid background in statistical analysis. Excellent interpersonal skills and the ability to interact effectively with members of the research teams are essential to the success of the individual in this position. The successful candidate must be able to learn and work independently, yet collaborate effectively with co-workers.
Duties include:

Employ new and existing tools to interpret, analyze, and visualize multivariate relationships in data.
Develop algorithms and statistical models, and perform statistical analyses appropriate to data.
Prepare figures for publication and presentation.
Collaborate with lab members on data collection and analysis methods.

*-Other duties may also be assigned. DESIRED QUALIFICATIONS:

Four-year college degree in bioinformatics, biostatistics, computational biology, computer science, or similar, with a strong publication record. Alternatively, a PhD in one of the aforementioned fields (or similar) combined with a very strong record of data analysis.
Working knowledge and a solid understanding of biological principles, bioinformatics terminology, and common sequence alignment programs (eg. Bowtie).
An understanding of the statistical principles behind current best practices in high-throughput molecular data analysis.
Strong experience in the use of a high-level programming language such as R, MATLAB, Python or Perl for complex data analysis.
Ability to generate novel pipelines to analyze unique types of data.
Proficiency with Unix systems and queuing systems (e.g. SLURM).
Familiarity with reproducible computing research paradigms including Docker.
Ability to provide advice to lab members on appropriate data analysis approaches, and to help implement them.
Ability and willingness to mentor junior students.
Ability to work both independently and collaboratively, and to handle several concurrent projects.
Exceptionally strong communication and interpersonal skills.
Excellent data presentation and visualization skills.
Ability to effectively present complex results in a clear and concise manner that is accessible to a diverse audience.


Bachelor's degree or a combination of education and relevant experience. Experience in a quantitative discipline such as economics, finance, statistics or engineering.


Substantial experience with MS Office and analytical programs.
Strong writing and analytical skills.
Ability to prioritize workload.




Sitting in place at computer for long periods of time with extensive keyboarding/dexterity.
Occasionally use a telephone.
Rarely writing by hand.

* - Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of his or her job.


Some work may be performed in a laboratory or field setting.

Job: Information Analytics

Location: School of Medicine

Schedule: Part-time

Classification Level:

To be considered for this position please visit our web site and apply on line at the following link:

Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.

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