IGENOMIX is a company that provides advanced services in reproductive genetics. Our broad experience and qualifications make us one of the global leaders in this field, and we guarantee we will offer you effective solutions to different infertility problems. Our bioinformatics department is seeking applications from capable candidates for a (bio)statistician position to be filled immediately.
Education and training: A PhD or MSc in Statistics, Biostatistics, Statistical Genetics, Applied Mathematics or similar subject. Advanced degree (PhD) is a strong plus. The position requires at least two years of translational (bio)statistics research in an academic/or biotech setting. Strong experience and proficiency with:
(1) A good understanding and ability to implement statistical techniques used in clinical studies/genomic research (e.g. logistic/linear regression, high-throughput data analysis with univariate and multivariate models taking into the account the complex correlation structure of the data, meta-analysis, survival analysis, statistical models for the analysis of longitudinal and event history data, etc.)
(2) machine learning methodologies, such as Random Forest, SVM, Logistic regression, nested loop cross validation, discriminant analysis.
(3) statistical software, such as R.
(4) understanding and expertise on NGS data analysis.
(5) programming languages, such as PERL or Python.
(6) Additional computational skills, such as bioinformatics, or shell scripting and automation on Unix/Linux systems.
(7) The ability to work closely with others as part of the group, while taking personal responsibility for assigned tasks. Ability to collaborate effectively with other computational and wet-lab scientists.
Job description: The employee will assist in developing methods to analyze and integrate various types of high-throughput molecular measurements including genetic, genomic, transcriptomic, miRNA, metabolomic, and clinical data. The employee of this position will be responsible to perform various statistical analyses of the “omic” data that has been generated in the lab as well as publicly available data.
If interested please send your CV with list of three references to firstname.lastname@example.org