Description
You should be someone who knows the difference between kinases and kinetics, gene expression and proteins, and asthma and arthritis.Key responsibilities:
Work closely with Software Engineers to architect, develop, maintain, and document a computational infrastructure that efficiently executes complex queries across many terabytes (likely petabytes) of disparate data and knowledge on genomics, genetics, pharmaceuticals, and chemicals.
- Propose, develop, and evaluate novel bioinformatics methods and techniques (including machine learning methods) to mine databases towards the identification of new drug-indication hypotheses.
- Present the work and results to pharmaceutical partners, and at conferences and workshops, and assist in the writing of grant proposals and business plans.
The ideal candidate will have broad expertise in biocomputation and software engineering. Specific requirements include:
- Proficiency in popular bioinformatics programming languages (e.g. Python, Ruby, Perl, C++, Java).
- Relational, non-relational, and semantic database management systems, such as MySQL or Oracle, HBase, MongoDB, Neo4J, OrientDB, and RDF engines.
- Common bioinformatics tools and algorithms, such as BLAST, SAMR, NCBI APIs, Cytoscape, etc.
- Service-oriented architecture use and implementation (e.g., REST, SOAP, Apache mods).
- Linux expertise and administration (AWS experience is a plus). Experience with high-performance computing systems and tools (e.g., Sun Grid Engine, Hadoop).
- Structured data formats such as XML, esp. those used in bioinformatics (e.g., FASTA).
- Expertise in the development and/or utilization of systems or network biology computational methods is strongly preferred
- Statistics and statistical programming languages, such as R, as well as machine learning and knowledge mining technologies, such as Weka.
- In-depth familiarity with computational and statistical methodologies for analysis of microarray, RNA-seq, and proteomic data. A plus would be experience in the development of molecular signatures distinguishing sample sets, such as the differential analysis of disease states based on transcriptomic data.
- Experience in chemoinformatics and drug knowledge bases is strongly preferred.
- In-depth familiarity with computational and statistical methodologies to identify disease causing targets and disease modifying drugs (e.g. Connectivity Map)
- The ideal candidate will have a working understanding of drug discovery and chemo-informatics technologies, gene expression analysis, proteomics and analysis of human genetic variation.
An MS or PhD in Bioinformatics, Biomedical Informatics, Computational Biology, or Computer Science with experience in Bioinformatics are essential, and 2-5 years in the biotech or pharmaceutical industry are preferred
To find out more about Real please visit www.realstaffing.com