Entry Level Genomics Data Scientist Jobs (NOW HIRING)

Brain Resource's mission is to make sense of the brain's complexities and provide a coherent model for measuring brain health. Healthcare requires evidence-based data and reliable benchmarks in order to improve the way decisions in brain health. Brain Resource established the first and largest international human brain database.

Our research sector conducted the International Studies to Predict Optimized Treatment (iSPOT) Response - in Depression and ADHD and is focused on a single goal: to improve the treatment outcomes for millions with Depression and ADHD by identifying objective tests to predict treatment response.

We are seeking a data scientist (statistician) to aid with the analysis of the data collected in our iSPOT studies (see http://www.brainresource.com/research/ispot). The Depression component of this study aims to identify biomarkers that can objectively predict which antidepressants are most likely to help which people with depression, and to develop a range of tests that have real-world application. The ADHD component aims to identify biomarkers that can predict which children/adolescents have ADHD and will respond to stimulant medications. Taking an integrative neuroscience approach, these studies span clinical, cognitive, psychophysiological, imaging and genomic data, and has 20 peer-reviewed publications to date. You will be involved in the design and running of analyses, and writing up these methods and results.
Additionally we require a range of analyses that further explore data in the Brain Resource International Database, which now has several hundred thousand datasets.
This is a high profile position in a world leading study, with a global multidisciplinary team from a range of academic institutions and backgrounds.
This position is based in San Francisco, CA.

Required Attributes
Graduate degree or equivalent, with experience in research and statistical analysis
Experience using the R statistical package (or Python/Scala) at an advanced level, including scripting, automated analyses and data visualization.
Strong experience with statistical data analysis for the social sciences or equivalent human clinical datasets
Sound knowledge of and experience with predictive statistics (with preferred experience in cross validation or similar techniques)
Experience with large datasets (with preference for multidisciplinary data experience)
Highly skilled in distilling complicated results and openly communicating challenges
Excellent communication and interpersonal skills, with demonstrated ability to liaise effectively with a range of people (e.g., senior management and other researchers from a range of disciplines)
Highly developed organizational skills, including prioritization and time management
Strong ability to work both independently and as part of a team
Demonstrated problem solving skills and a strong focus on solutions
Ability to contribute to publications
Ability to present outcomes to a range of interested audiences
Preferred Attributes
Preferred statistical experience includes a good understanding and use of logistic regression, predictive models, multiple imputation, spline regression, linear mixed models for dose/regime type trial designs, simulations, cross validation, correction for multiple testing, principle components analysis.
Experience with bash scripting and pipeline development. programming for parallel computing
Experience in pharmaceutical or large clinical trials, particularly in the planning, design or data analysis.
Experience with submissions to the FDA
Experience in the field of neuroscience, psychology or psychometrics.
Knowledge of genetics microarray/SNP data or EEG data analysis

This is a full-time position. Salary will be dependent on the experience and skills of the applicant. The position is located at our San Francisco office in the Financial District.
To apply, please include a CV and cover letter addressing the necessary skills and experience outlined above. Applications and further enquiries should be directed to:  Donna Palmer.