Machine Learning & Algorithms Data Scientist

Machine Learning & Algorithms Data Scientist

* LOCAL CANDIDATES ONLY* The role is mostly remote with some instances to collaborate in person. Roughly 1 or 2 times monthly in office. 5% - 10 % travel per month

Interview process: Panel interview (1–2 interviewers), ~1 hour, in-person only (no virtual interviews).

TOP THINGS:
o Operational experience in building and tuning ML models (regression, random forest, XGBoost, etc.).
o Python proficiency; experience with Jupyter notebooks.
o Experience with Foundry (for model operationalization and version control) is desirable but not mandatory.
o Hands-on experience building and deploying ML models for real world applications.
o Understanding of model evaluation, optimization, and feature engineering

- Mathematics & Statistics
o Solid foundation in probability, linear algebra, calculus, and statistical inference.
- Programming Proficiency
o Strong command of languages like Python, R, and SQL for data manipulation and model development.
- Presentation Skills
o Provide insights in clear and concise manner for upper management review through various mediums such as graphics, power point presentations, dashboards, etc
- Domain Knowledge (Additional Desired Skills)
o Understanding of Utility data, has industry context, project management methodologies, Foundry experience.

Position Summary:
We are seeking an experienced Data Scientist who will provide strong execution and delivery of data science. Working as a part of the Electric Operations Excellence Team, this Data Scientist will translate business needs into advanced analytics and machine learning models. The successful candidate will be responsible for model selection and identification of appropriate training data sets; building, training, and evaluating models; and delivering results to the business on a regular cadence. This role will work alongside a product owner, technical lead, and with internal stakeholders, process owners to support delivery of high-value analytics machine learning models to derive key insights for execution of the work within Electric Operations.

Position Responsibilities:
� Leads development of high complexity models and training sets
� Provides hands-on execution and implementation of data science models
� Translates business analysis needs into well-defined data science problems, and selecting appropriate models and algorithms and communicates model evaluation and implications of results back to stakeholders
� Recognizes and prioritizes the most important work related to data science models to achieve highest operational impact for analytics in the business
� Balances tradeoffs among analytics value, model development methods and design and technologies used to implement data science models with a bias toward action
� Performs collaborative work on data science problems and mentor junior data scientists
� Creates shared process models, business objects, activity diagrams and process documentation to effectively articulate multiple views of the business solutions that support technical architecture.
� Manages development of quantitative models and tools.
� Collaborates with leaders, other LOBs, and business partners to work on issues, projects or activities.
� Develops new or revises complex models to predict business demand trends, and volume and expenditures forecasts capacity analysis, and various other metrics to identify potential opportunities.
� Assesses business implications associated with modeling assumptions, inputs, methodologies, technical implementation, analytic procedures and processes, and advanced data analysis.
� Partners with leaders to drive high performance in their lines of business.
� Develop deep understanding of business drivers and financial levers to provide strategic decision support.
� Oversees resolution of complex projects and programs.
� Develops and maintains up-to-date detailed project schedules and work plans.
� Performs analysis on complex data models requiring customized reports and data and presents recommendations.

Bachelor's Degree in Econometrics, Economics, Engineering, Mathematics, Applied Sciences, Statistics or job-related discipline or equivalent experience
Job-related experience, 8 years, OR Master
�Degree and job-related experience, 6 years, OR Doctorate Degree and job-related experience, 3 years. Experience in data modeling, 5yrs

Desired Education / Skills:
PhD in engineering or a related field (computer science, natural sciences, mathematics)
Experience with Python, R, Scala, SQL
Experience developing solutions with Pandas/Scikit-learn, Spark or comparable technologies
Experience data science notebooks (Jupyter, Zeppelin or other)
Experience with AWS, Azure, 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 as needed to perform at the job level
Knowledge of relevant programming languages - for example Visual Basic, Ladder Logic,
Programmable Logic Controller, C, SharePoint, HTML, Java, Adobe - as needed to perform at the job level
Competency in knowing the most effective and efficient processes to get things done, with a focus on continuous improvement
Knowledge of principles, techniques, and procedures used for production and design of technology-based equipment and systems as needed to perform at the job level
Knowledge of statistical theories, concepts, methods, best practices, and analyses as needed to perform at the job level
Ability to develop reports, models, and simulations as needed to perform at the job level
Competency in developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences
Knowledge of data model design philosophies and methodologies for data warehouse and OLTP systems