How Data Analyst should be in Renewable Energy Industry/Laboratory

In this blog, I would like to share a middle/high level job opportunity in The National Renewable Energy Laboratory (NREL), is a leader in the U.S. Department of Energy's effort to secure an energy future for the nation that is environmentally and economically sustainable. When I was searching some information related with the data mining usage in renewable energy, I found a job posting in NREL website. Then, I wanted to share this job position with its job summary, duties and required things from the applicant since those things in the job position is a kind of very good summary to understand what the data mining analyst can do in renewable energy industry or laboratory. It is interesting for me because I need more information about what can be done using data mining in some area like energy or manufacturing industry other than Google or other internet based applications. In addition to learning what data analyst can do in energy industry or laboratory, I also got some specific qualifications expected from the data analyst such as which software or tools are needed and also it gives some idea which tools are used commonly in these areas.

Data Scientist

Job/Research Summary
The National Renewable Energy Laboratory (NREL) located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for research, development, and deployment of renewable energy and energy efficiency technologies. As part of its mission supporting the transformation of the energy economy, NREL is expanding the role of statistical methods in its analytic work and has an immediate opening for a full time Postdoctoral Researcher in that field. The successful applicant for this position will perform research on the use of modern statistical techniques in the analysis of data sets relating to renewable energy and energy efficiency. Initially, the research will focus on anomaly detection, imputation of missing data, analysis of correlations, and development of models for data describing and forecasting renewable energy resources (wind, solar, and biomass potential over space and time), energy demand (e.g. customer load on the electric power grid), and explanatory variables (e.g., atmospheric phenomena). Although this work will be done in collaboration with a team of renewable energy researchers, scientists, engineers, and modelers, a high degree of independence and leadership is required for this position. The initial appointment is for one year, depending on funding, with possible renewal for up to maximum of three years.

Job Duties
(i) Develop and validate multivariate statistical models of spatiotemporal renewable energy fields, based on data sets of disparate spatiotemporal resolution and extent. The models may be applied to spatiotemporal interpolation problems, to forecasting, to understanding correlations in extreme values, and to the reconciliation of conflicting data sources.
(ii) Develop statistical techniques for assessing and comparing data quality in renewable energy data sets. This includes the identification and correction of biases in such data sets.
(iii) Provide general statistical support to a diverse set of projects involving modeling, simulation, and analysis related to renewable energy, with an emphasis on the analysis of large and complex data sets.
(iv) Prepare technical reports, journal articles, and presentations summarizing statistical techniques and analysis results.

Required Education and Experience
Must be a recent PhD graduate within the last three years.

Additional Required Knowledge, Skills and Attributes
(i) Must have experience applying regression techniques and other statistical methods to large data sets.
(ii) Must have demonstrated facility with one or more programming languages (general purpose or statistical) suitable for the statistical analysis of large data sets.

Preferred Qualifications
(i) Experience applying statistical analysis to energy resource, renewable energy, and/or atmospheric science data sets.
(ii) Training or experience in the use of (a) spatiotemporal statistics, (b) time series analysis, (c) extreme value theory and risk analysis, (d) Bayesian methods, (e) multivariate regression, (f) sensitivity analysis, or (g) uncertainty quantification.
(ii) Familiarity with the use of relational databases, geographic information systems, and data visualizers.
(iv) Familiarity with a programming language like R, Python, Java, or C++.

http://www.kdnuggets.com/jobs/12/02-13-nrel-postdoc-researcher-data-scientist.html