Database Programmer / Data Scientist
CurrentOverview• Collaborate with scientists to develop new analyses• Implement and distribute in-house analysis tools• Code applications as products for release to customers• Curate tens of thousands of records in a number of different databases• Analyze pathways using Kyoto Encyclopedia of Genes and Genomes (KEGG) and iPATH interactive metabolic pathway explorer• Troubleshoot the most difficult customer issues for the Technical Assistance department• Engage in all aspects of software developmentDetails• Principal Components Analysis, ranking characteristics by their discriminating power• Student T, comparing two samples with many characteristics• AnOVa (Analysis of Variance), comparing three or more samples with many characteristics• Probability Distributions, normalizing characteristics to scores between 0.0 and 1.0 for thresholding• Curve Fitting• Enterprise-level MS SQL server application• Cluster Analysis (Dendrograms), assigning samples to groups• Consensus Analysis, comparing two samples with many characteristics• Microbial Identification• Phenomics (Phenotype Micro Array)• Collecting and writing specifications• Designing Database Schemas• Designing User Interfaces• Designing Algorithms• Coding and Testing• Debugging the most difficult bugs in code• Writing documentation• Deploying, installing, and configuring• Troubleshooting after deployment (designing workarounds)ExampleTook algorithm design one step beyond. Not content merely to implement Discriminant Analysis, which gives N – 1 components, in which N is the number of user-assigned groups, I figured out how to rank and weigh the components by their discriminating power, thereby increasing the utility of the analysis many-fold.
