To develop novel tools for bioinformatic analysis of metabolomic data generated by mass spectrometry and to analyze and manage the data in useful biomarker profiles for drug screening, diagnostic tools and potential therapeutic targets. The candidate should have a track record demonstrating the ability to be self-motivated and self-directing, to develop realistic project plans with timelines, and to solve problems using application of scientific theory.

• Assist in the experimental design of projects.
• Design and implement statistical analysis of metabolomic data.
• Carryout both multivariate and univariate statistics applicable to biomarker discovery and validation.
• Evaluate and development internal data analysis tools to increase the throughput of discovery based experiments.
• Maintain and analyze very large and dynamic datasets.
• Work closely with biologists and analytical chemists.
• Write grant applications at the direction of the Chief Scientific Officer in collaboration with other scientific staff.
• Keep and maintain accurate and detailed records of data analysis procedures and results.
• Contribute to the development of novel algorithms and data visualization tools.

• Masters degree in Biostatistics or related discipline with a background in “omics” type data analysis methods.
• A strong knowledge of statistical programming in R and experience in Matlab or Mathematica.  The ability to use Perl and other scripting languages are desired, but not required.
• At least two years experience in bioinformatics research and development, preferably in metabolomic or proteomic data analysis.
• An understanding of mass spectrometry based data analysis and manipulation is desired, but not required.

Experimental design and analysis of large scale LC-MS based metabolomics experiments for the identification of biomarkers, creation of predictive models, and biological network based interpretation of the results. The candidate should have a track record demonstrating the ability to be self-motivated and self-directing, to develop realistic project plans with timelines, and to solve problems using application of scientific theory.

• Design and implement small molecule and metabolomic analysis.
• Assist in design and execution of scientific experiments in collaboration with analytical chemistry and cell biology staff.
• Evaluate and develop new and advanced bioinformatics tools, making them accessible to internal users.
• Contribute to the development of visualization tools for complex biological data and novel algorithms to solve bioinformatics problems.
• Evaluate metabolomics using network analysis methodologies.
• Perform comparative analyses of mass spectrometer derived metabolomic data.
• Develop predictive models based on the results of metabolomics experiments.
• Work closely with biologists and analytical chemists in updating and utilizing multiple sources of biological data.
• Write grant applications at the direction of the Chief Scientific Officer in collaboration with other scientific staff.
• Keep and maintain accurate and detailed records of all research and experiments performed.
• Maintain current technical procedures and quality assurance.
• Attend staff and lab meetings.

• Ph.D in Statistics, Biostatistics, Computational Biology or, a molecular biotechnology or related discipline with a research focus in the area of statistics.
• A minimum of 5 years post graduate experience designing, analyzing, and interperting large "omics" based data sets preferably in the areas of metabolomics, proteomics, or gene expression. Proficiency with the statistical programming language R.
• Demonstrated experience in the generation of predictive models from experimental "omics" data utilizing univariate, multivariate, and machine learning methods.
• Experience with network based analysis of "omics" data.
• Strong background in biochemistry, specifically understanding of the metabolic pathways is required.
• Broad understanding of bioinformatics tools used for analysis of metabolic, gene expression, and protein expression data.

• Effective interpersonal, verbal and written communication skills, including public-speaking and presentation skills.
• Ability to maintain high level of confidentiality.
• Ability to effectively communicate with all levels of the organization.
• Demonstrated ability to work well independently as well as a part of a diverse team.
• Ability to maintain experimental consistency.
• Demonstrated ability to work well under pressure and manage multiple tasks with constantly changing priorities.
• Demonstrated ability to troubleshoot problems and recommend actions.
• Knowledge of implementation and design of state-of-the-art algorithms for metabolomic, genomic and metagenomic analysis.
• Advanced analytical, organizational, and record-keeping skills.
• Proficient computer skills: Proficiency with the Windows and Linux operating systems and MS Office.