Senior Biostatistician (3268) | GenomeWeb

Senior Biostatistician (3268)

Job Location
Frederick, MD
Job Description

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.

- Statistical analysis of complex high-dimension biological measurements (e.g. single-cell RNA-seq).
- Apply methods for inference of biological networks, e.g. gene co-expression, protein-protein-
interaction, cell signaling, etc.
- 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).
- Use state of the art statistical methods to analyze gene expression measurements.
- Build data analysis pipelines that leverage algorithms developed by the academic community, but that
are also customized to specific applications.
- Monitor genomics literature to understand the market for applications of sequencing and expression
- Communicate with customers and collaborators regarding their data analysis needs.


- Ph.D. degree in Statistics, Biostatistics, Bioinformatics, Computational Biology, Computer Science, or related discipline.
- Minimum of four years of daily experience analyzing high-dimension biological measurements.
- Successful experience using statistical methods for biological network inference (e.g. Bayesian networks) and functional genomics.
- Experience using methods such as factor analysis, expectation maximization, ensemble-of-forests, non-linear regression, mixed effects models, 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.
- Experience with transcriptome analysis: splice variant and fusion quantification, differential expression analysis, etc.
- Skills using existing high-throughput sequencing analysis tools are a plus (e.g. SAMtools, bedtools, IGV, BWA, GSNAP, eXpress, RSEM, etc.).

Personal Requirements:

Strong ability to communicate effectively with customers, collaborators, and the scientific community.

How to Apply

If interested, please apply online: or forward your resume to

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.

About Our Organization

QIAGEN, Inc. is a Biotechnology leader in the manufacturing of innovative, customer-driven products. Our rapid growth means that we are continuously seeking highly motivated and talented individuals to expand our operations. If you are interested in belonging to a dynamic and enthusiastic high-performing Team, developing your potential and using your skills to contribute to its success, QIAGEN, Inc. is where you should be! We recruit employees for the long term and reward them for their commitment.

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