We are seeking an experienced Data Scientist to join the Electric Operations Excellence Team. This role involves translating business needs into advanced analytics and machine learning models, with a focus on strong execution and delivery of data science solutions.

Responsibilities

Lead the development of highly complex models and training sets.
Provide hands-on execution and implementation of data science models.
Translate business analysis needs into well-defined data science problems, selecting appropriate models and algorithms, and communicating model evaluations and implications to stakeholders.
Recognize and prioritize critical data science work to achieve the highest operational impact for analytics in the business.
Balance tradeoffs between analytics value, model development methods, design, and technologies for implementing data science models, with a bias towards action.
Perform collaborative work on data science problems and mentor junior data scientists.
Create shared process models, business objects, activity diagrams, and process documentation to articulate business solutions supporting technical architecture.
Manage the development of quantitative models and tools.
Collaborate with leaders, other lines of business, and business partners on issues, projects, or activities.
Develop new or revise complex models to predict business demand trends, volume, expenditures, and capacity analysis for identifying opportunities.
Assess business implications of modeling assumptions, inputs, methodologies, technical implementations, analytic procedures, and advanced data analysis.
Partner with leaders to drive high performance in their lines of business.
Develop a deep understanding of business drivers and financial levers to provide strategic decision support.
Oversee the resolution of complex projects and programs.
Develop and maintain up-to-date detailed project schedules and work plans.
Perform analysis on complex data models requiring customized reports and data, presenting recommendations.

Requirements

Bachelor's Degree in Econometrics, Economics, Engineering, Mathematics, Applied Sciences, Statistics, or a job-related discipline, plus 8 years of job-related experience; OR Master's Degree plus 6 years of job-related experience; OR Doctorate Degree plus 3 years of job-related experience.
5+ years of experience in data modeling.
Strong command of languages like Python, R, and SQL for data manipulation and model development.
Hands-on experience building and deploying ML models for real-world applications.
Understanding of model evaluation, optimization, and feature engineering.
Solid foundation in probability, linear algebra, calculus, and statistical inference.
Ability to provide insights in a clear and concise manner for upper management review through graphics, PowerPoint presentations, dashboards, etc.
Willingness to travel to local Bay Area office locations; role is hybrid with 20% in-person reporting once a week in Dublin innovation or Bay Area office.
Availability for in-person interviews within 2 weeks of application submission (no exceptions).

Nice to Have

PhD in engineering or a related field (computer science, natural sciences, mathematics).
Experience developing solutions with Pandas/Scikit-learn, Spark, or comparable technologies.
Experience with data science notebooks (Jupyter, Zeppelin, or others).
Experience with AWS, Azure, or other cloud computing technologies.
Scrum team experience.
Energy industry experience.
Experience designing efficient data science workflows and database architecture for data science purposes.
Experience with forecasting, Bayesian networks, and graph analytics.
Strong statistics experience.
Experience with software development methodologies and software engineering principles.
Knowledge of program management theories, concepts, methods, best practices, and techniques.
Knowledge of relevant programming languages (e.g., Visual Basic, Ladder Logic, Programmable Logic Controller, C, SharePoint, HTML, Java, Adobe).
Competency in efficient processes and continuous improvement.
Knowledge of principles, techniques, and procedures for production and design of technology-based equipment and systems.
Knowledge of statistical theories, concepts, methods, best practices, and analyses.
Ability to develop reports, models, and simulations.
Competency in developing and delivering multi-mode communications to different audiences.
Knowledge of data model design philosophies and methodologies for data warehouse and OLTP systems.