Sponsor: Rubicon Genomics
Recording Date: 2/19/2014
Recording Time: 1 hour
We are currently seeking an entry-level Ph.D. statistician to work in our in Frederick, Maryland office. The statistician will analyze gene expression measurements (RNA-seq, qPCR, microarray) and DNA sequencing reads to support new product development. This position requires both adapting existing algorithms and developing new algorithms for optimal use with specific data types and specific scientific problems. The position requires a strong combination of statistical analysis skill, biology knowledge, and programming ability; however, depth of skill and prior accomplishment in statistical analysis is more critical than biology knowledge or programming skill.
- Use state of the art statistical methods to analyze gene expression measurements.
- Statistical analysis of complex high-dimension biological measurements (e.g. single-cell RNA-seq).
- Build data analysis pipelines that leverage algorithms developed by the academic community, but that are also customized to specific applications.
- Actively publish in peer-reviewed journals in system biology, functional genomics, and bioinformatics.
- Evaluate emerging trends in applications (e.g. single-cell gene expression) and methods (e.g. probabilistic splice-graphs for estimating transcript abundance).
- Monitor genomics literature to understand the market for applications of sequencing and expression technology.
- Apply methods for inference of biological networks, e.g. gene co-expression, protein-protein-interaction, cell signaling, etc.
- Communicate with customers and collaborators regarding their data analysis needs.
- PhD degree in Statistics, Biostatistics, Bioinformatics, Computational Biology, Computer Science, or related discipline.
- Minimum of four years of daily experience analyzing high-dimension biological measurements.
- Experience with transcriptome analysis: splice variant and fusion quantification, differential expression analysis, etc.
- Successful experience using statistical methods for biological network inference (e.g. Bayesian networks) and functional genomics.
- Experience using methods such as regression, factor analysis, expectation maximization, ensemble-of-forests, etc.
- Strong skills in statistical classification, including feature selection. Demonstrated successful application of methods such as Random Forest, SVM, generalized linear models, HMM, etc.
- Proficiency with statistical analysis using R, Matlab, or Python numpy/scipy/matplotlib.
- Skills using existing high-throughput sequencing analysis tools are a plus (e.g. SAMtools, bedtools, IGV, BWA, GSNAP, eXpress, RSEM, etc.).
- Skills using high-throughput sequencing analysis tools are a plus (e.g. SAMtools, bedtools, IGV, BWA, GSNAP, eXpress, RSEM, etc.).
Strong ability to communicate effectively with customers, collaborators, and the scientific community
If interested, please apply online:
or forward your resume to firstname.lastname@example.org
As the innovative market and technology leader, QIAGEN creates sample and assay technologies that enable access to content from any biological sample.
Our mission is to enable our customers to achieve outstanding success and breakthroughs in life sciences, applied testing, pharma, and molecular diagnostics. We thereby make improvements in life possible.
Our commitment to the markets, customers, and patients we serve drives our innovation and leadership in all areas where our sample and assay technologies are required.
The exceptional talent, skill, and passion of our employees are key to QIAGEN’s excellence, success and value.