OBJECTIVE:

Employ computing/development and quantitative skills to support a company leveraging cutting - edge technologies.

SUMMARY:

I earned a Master of Science degree in Statistical Science and earned a Bachelor of Arts degree from a top school. In academic research activities, I have employed special cases of linear models and model parameter estimation via simulation. I had experience teaching, tutoring, publishing research findings under peer review, and I have won academic awards and grants. Professionally, I have had some years experience in developing ETL’s for cloud-based big data backends along with frontend web stack applications and statistical computation/visualization layers.

TECHNICAL SKILLS:

Quantitative: Numerical data summaries and visual data display, the Empirical Rule, z-scores, frequency tables, basic probability and Bayes’ Theorem, probability distributions, test statistics, inference, t-tests, least squares estimation, analytical and numerical maximum likelihood estimation, Bayesian Markov chain Monte Carlo parameter estimation, data cloning, special cases of regression, ANOVA, simple linear regression, multiple linear regression, logistic regression.

Computing: Competency with: The SAS software, the R Language, Python, Linux, SQL (PostgreSQL, SQL Server, Oracle, MySQL), triplestores with SPARQL and column store databases, Django, Linux, Bash, Javascript, JSON, XML, HTML, CSS, SCM/Git.

Familiarity with: Perl, PHP, Apache, NodeJS, C, C++, Haskell, Scala, GCC.

EXPERIENCE:

Confidential

Software Developer, Research and Development (Computing)

Responsibilities:

• Aggregated disparate but related data sources into persistence for frontend developers.
• Developed stack that aggregates and delivers data and analytics tools.
• Wrote/executed multiple source-system extractors that load data in a variety of formats into Amazon S3 storage with provenance.
• Loaded/managed 3.3 million files in storage.
• Transformed/coalesced data sources into aggregate data stores on Linux Amazon EC2 instances, including triplestore graph databases, a column store, and SQL databases.
• Wrote frontend applications using R Shiny, HTML, CSS, and Javascript to browse, filter, and analyze data via custom routines and made results persistent in static HTML and database for placing in larger context for discussion and streamlined workflow.

Technologies used: graph databases (4store), Linux, Python, Haskell, MPI, HDF5, Protocol Buffers, PostgreSQL, SQL Server, Oracle, MonetDB with R and Python integrations, AWS S3, AWS EC2, .NET for developing with proprietary database API, REST, Microsoft Access and Excel extractions, NodeJS, et al. Technology-rich shop. SCM via Git in BitBucket and Agile via JIRA.

Lead Data Integrator and Scientist, Research and Development

Confidential

Responsibilities:

• The main duties included working directly with the scientists and engineers looking to aggregate, analyze and visualize their data sets aligned with an overall process, knowledge and understanding (PKU) initiative.
• The role required both in depth understanding of underlying material and process engineering and science, aggregation, analysis and visualization strategies. Individual should be self-sufficient to help drive the project through the process to delivery and training on the process knowledge and understanding methodology.
• Map out scientific business processes to put context on data via recipe framework.
• Gather current data framework and perform aggregation in concert with the scientists.Perform statistical analysis and provide consultation on the results to the scientists.
• Define and help drive the information architecture in a scalable manner.

Confidential

Database and Web Developer

Responsibilities:

• My main duty was to develop/maintain two web applications for the group, i.e. the group's website and a web application to allow “staff” to register new data in the field via web frontend and facilitate front end queries for academic publications.
• Also, I worked to inventory and profile digital assets on network partitions. Technologies used include: Unix, Linux (CentOS), Apache, MySQL, PostgreSQL, Python/Django, PHP, HTML, CSS, Javascript, and Git.

Confidential

Data Analyst

Responsibilities:

• Modified and executed archived SAS/SQL programs to convert raw financial data from a variety of formats into SAS datasets.
• Wrote programs to generate webforms based on data that help provide a web interface for customers submitting data queries online; put SAS query drivers in place to be executed by customers through the website. SAS query drivers are passed parameters submitted from the customer through the web interface.
• Wrote new conversion code for cases in which datasets were added to the collection of those already available through the website.
• Completed special side tasks such as debugging outdated, free example SAS code for customers to follow/execute and writing a Python script to identify query drivers that called a deprecated macro and upgraded those with a new version.

Confidential

Responsibilities:

• Created the program using the R Language and extended computationally intensive (non-vectorizable) pieces of the program by creating R packages written in C++ through the Rcpp facility. The extensions can be built and installed on other machines.
• Improved computation time by pushing instances of a worker class into separate threads under the Qt/C++ framework. I used the low-level QThread API rather than the higher-level multithreading APIs offered under Qt. File reading time converged to 8 minutes from 13 with 3 worker threads.
• Implemented suggestions throughout the development, e.g. I employed random number generation to allow sampling of qualifying stock-days, designed graphical representations of results, and incorporated a missing candlestick imputation algorithm based on defined assumptions.

Confidential

Responsibilities:

• Held discussions with inquiring students on a variety of topics, including numerical data summaries and visual data display, the Empirical Rule, z-scores, regression, frequency tables, basic probability and Bayes’ Theorem, probability distributions, test statistics, inference, t-tests, and ANOVA.
• Held informal discussions with students in upper-level life science courses critiquing methods in scientific publications and planning research.
• Assisted students familiarizing with statistical computing tools: the R Language, the SAS system, Microsoft Excel, and Minitab; showed students how to reference software documentation to solve computing questions. SAS instruction included creating datasets, data step programming, PROC MEANS, PROC GLM, PROC LOGISTIC, PROC TTEST, PROC PRINT, et al.

Research Assistant

Confidential

Responsibilities:

• Motivated by the fact that foresters and ecologists in the Inland Northwest U.S.A. are challenged to predict tree nutrient deficiencies in any particular tree stand. A research group had serially deployed small, multinutrient fertilizer screening experiments across the variety of soil types.
• The data from those experiments were compiled into a database for analysis.
• Researched stochastic growth models and selected the linear mixed effects model to explicitly model the variation in growth between experiments while testing the hypothesis of significant fertilizer-soil interaction across the tree population. Statistical modeling was conducted using PROC MIXED in SAS with the post hoc LSMEANS option.
• Reported regularly to advisers on the status of the project.
• Disseminated results in presentation form at a national conference and published a peer-reviewed paper describing the research and results.

Teaching Assistant

Confidential

Responsibilities:

• Provided a weekly overview of the tasks in the current assignment and answered students’ questions as they worked through it.
• Verified that laboratory instructions were valid under updated versions of a variety of software packages prior to distribution to students.
• Graded assignments and proctored and graded exams.
• Taught about 40 students.

Confidential

Teaching Assistant/Grader

Responsibilities:

• Provided a weekly overview of the tasks in the current assignment and answered students’ questions as they worked through it.
• Graded assignments and proctored and graded exams.
• Taught about 50 students.

Research Intern

Confidential

Responsibilities:

• My duties included assisting in execution of an experiment, analyzing collected data, and disseminating results to scientists.
• Motivated by importance of understanding the implications of nitrogen farm fertilizer run-off for marsh ecosystems now and in the future when there will be higher atmospheric carbon dioxide concentrations.
• Measured vegetation periodically to estimate growth rates and monitored delivery of carbon dioxide for treatment quality assurance.
• Entered data into Microsoft Excel, and analyzed data using PROC GLM in the SAS system.
• Used the appropriate analysis; the experiment was a 2x2 factorial with treatments randomly assigned to units within blocks that transected an elevation gradient.
• Disseminated findings in a professional poster presentation at the Annual Society of Wetland Scientists Conference and published in a peer-reviewed science journal.