Data Scientist

Data Scientist

The full-time contract Data Scientist assignment will work within ***'s (BPA's) Transmission Infrastructure Asset Management (TI) organization on the Strategy and Planning (TIS) team. This assignment will provide support for projects under the TI program by providing analytical expertise for planning, development, integration, and implementation of asset management technologies, methods, and standards for interim and long-term solutions to manage risk and spend efficiency on BPA's transmission system. This assignment will analyze, process and model data and interpret the results to develop data-driven solutions.

Candidates should be self-starters, motivated to work independently in a collaborative manner with their own team members and internal stakeholders. The ideal candidate should enjoy working under pressure and timelines, while maintaining accuracy and excellent records management. This position is very busy, resides in a fast-paced environment, and manages multiple projects/tasks with the ability to adapt to change.

Works closely with asset management teams to identify and answer crucial strategic asset management questions, leading efforts to provide data-driven insights that support and inform broad strategic initiatives. Drives the development of innovative data solutions to extract, analyze and model data to solve or answer previously unanswered problems of a complex and nuanced nature. Introduce new analytical and predictive models and methodologies that serve to establish new best practices in day-to-day operations. Employs mastery of a broad range of advanced methods in mathematics, statistics, computer science, and machine learning to develop best-in-class analytical techniques for modeling complex patterns in a variety of data types. Develops and recommends innovative approaches and new solutions for data access, maximization and utilization and identifies emergent trends and opportunities for future development. Performs research to design and implement efficient algorithms for extracting information from large quantities of raw data across numerous disparate and potentially conflicting data sources. Performs independent studies and assessments to evaluate the effectiveness and efficiency of new computational and storage technologies in comparison to current systems capabilities. Provides useful visuals and executive summaries as a technical advisor to senior management and other stakeholders. Explains and shares model output to impacted and interested operational and executive parties and stakeholders. Develops and applies innovative statistical and mathematical principles and concepts along with appropriate testing and prototyping programs. Provides technical expertise on analytics that address BPA and industry specific questions. Create procedural and technical documentation of models before handing over routine model running to reporting personnel. Reviews analytics created by others, provide advice and consultation, and lead independent verification of results, when needed. Facilitates coordination of assumptions, data and selection of methodologies.

A Bachelor's or Master's degree in advanced mathematics, computer science, machine learning, or statistical methods is required: With a Master's degree, 7 years' of hands-on experience performing the following is required With a Bachelor's degree, 9 years' of experience is required: Manipulating data sets, querying databases, and building statistical models Statistical or data mining techniques Using Web Services Analyzing data from 3rd party users Developing data models and algorithms Creating and using advanced machine learning algorithms and statistics Knowledge and understanding of financial analysis/budgeting, risk analysis, probability and statistics, and electric utility operations With a Bachelor's degree, at least 10 graduate credits in computer science algorithms, statistics, software design, or data management OR one of the following Data Science Certifications or similar are also required: Certified Analytics Professional (CAP) Data Science Council of America (DASCA) Senior Data Scientist (SDS) Data Science Council of America (DASCA) Principle Data Scientist (PDS) Dell EMC Data Science Track Google Certified Professional Data Engineer Google Advanced Data Analytics Certificate for Machine Learning IBM Data Science Professional Certificate Mathematics experience including multivariate calculus, linear algebra, differential equations, and real analysis: Probability and Statistics: including stochastic processes, classical inference techniques, maximum likelihood estimation, Bayesian methods, Monte Carlo, and bootstrapping. Computer Science: design and analysis of algorithms and data structures, computational complexity, search methods. Supervised Learning (e.g., regression techniques, regularization techniques, ridge regression, ensemble methods, optimization through linear programming and convex optimization, nonlinear programming). Unsupervised Learning (e.g., clustering techniques, hierarchical clustering, dimensionality reduction, principal component analysis). Time Series Analysis. Demonstrated knowledge of computer languages including, but not limited to Python, Java, SQL, and R. Demonstrated knowledge of distributed or parallel processing techniques used in the analysis and processing of large data sets. Skill in discerning the strengths and weaknesses of various best-practice quantitative solutions for a given real-world problem and skill in developing new quantitative approaches to cater to particular features as needed when standard assumptions are inappropriate. Using considerable judgment, proven ability to take vague or broadly defined goals or business objectives and translate them to questions that can be answered or problems that can be addressed via data driven analysis. Demonstrated ability to communicate and present proposals, findings, and recommendations, both written and orally, to senior staff, management and executives and to external parties (e.g., representing BPA regionally such as to key stakeholders, customers, industry organizations, or regulators). Knowledge of GIS and Asset Management Systems. Experience with R software product(s). Energy/utility industry experience. Experience with Power BI (Microsoft Business Intelligence).