Sr. Data Scientist - Analytics; Hybrid

Sr. Data Scientist - Analytics (Hybrid)
Our client, a leading Pacific Northwest utility provider committed to public service and environmental preservation, is looking for a Sr. Data Scientist - Analytics for their Vancouver, WA location. This
position will work within the Transmission Infrastructure Asset Management organization on the Strategy and Planning team. Support projects under the 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 the transmission system. Will analyze, process and model data and interpret the results to develop data-driven solutions.

This hybrid position is a one-year renewable contract that includes employee benefits! If you feel this position aligns with your professional experience, we want to hear from you!

Key Responsibilities for Sr. Data Scientist - Analytics:

• 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.
• 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.
• Reviews analytics created by others, provide advice and consultation, and lead independent verification of results, when needed.

Requirements/Qualification for Sr. Data Scientist - Analytics:

• 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.
• 7+ years of hands-on experience in the following skills:
  • 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 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…