About us

National Grid is hiring a Lead Data Scientist for Short-Term Load Forecasting. This position can be based out of Waltham, MA or Hicksville, NY office.

Every day we deliver safe and secure energy to homes, communities, and businesses. We are there when people need us the most. We connect people to the energy they need for the lives they live. The pace of change in society and our industry is accelerating and our expertise and track record puts us in an unparalleled position to shape the sustainable future of our industry. 
To be successful we must anticipate the needs of our customers, reducing the cost of energy delivery today and pioneering the flexible energy systems of tomorrow. This requires us to deliver on our promises and always look for new opportunities to grow, both ourselves and our business.
 

Job Purpose

The Load Forecasting & Analytics team produces forecasts for customers’ electric and gas consumption within National Grid’s US service territory.  As a Lead Data Scientist for Short-Term Forecasting, you will build short-term (i.e., intra-day to weeks-ahead) electric & gas load forecasts at the system, zonal, and local distribution feeder levels. The forecasts will be used for supply procurement, operation, engineering, demand-side management, and distributed resources management to improve efficiency, flexibility, safety, and reliability of our systems and assets.  To develop these forecasts, you will apply machine learning or statistical modeling approaches, integrating knowledge of our electric and gas networks and distributed energy resources (including solar and wind generation, demand response, electric vehicle technologies, and battery storage). Your work will support internal stakeholders including Supply, Engineering, Operations, and others. You will work with the stakeholders to collect their business needs, develop project scope, implement projects accordingly, and deliver the results.  

Key Accountabilities

The position of lead data scientist requires the ability to work in team-oriented environments as well as independently, taking initiative and engaging the diverse expertise of team members and stakeholders to the benefit of the projects for which the lead is responsible.  The ability to speak to both technical and non-technical persons to present projects is essential, as the position requires communicating to both internal and external stakeholders to present and explain results.

You will be responsible to:
•    Drive the development of short-term forecasts for gas consumption, electric power usage, and distributed electric generation at both the system level and more geographically granular levels including towns and neighborhoods.
•    Build predictive models utilizing multiple linear regression, times series analysis, regression-based machine learning, and/or deep learning, while staying up to date with new developments in these technical areas. 
•    Support end users with monitoring and analysis of predictions, including forecast performance, interpretation, root-cause analysis, and anomaly detection, to ensure our deliverables meet their needs. 
•    Ensure the forecasts incorporate distributed energy resources (DERs) like solar, wind, battery storage, demand response, electrification, and other new technologies  for a variety of use cases.
•    Initiate process improvement and implement industry best practice.
•    Collaborate cross-functionally with data engineers, software developers, and business owners to ensure the broader forecasting pipeline, process, and documentation are efficient, clear, repeatable, and reliable.
•    Code collaboratively, contributing to shared code repositories and providing thorough code reviews
Update forecast processes and documentation to ensure they are efficient, reviewable, and reliable.
•    Support other load forecasting teams – long-term electric & gas, data and DERs – on ad-hoc basis and other projects as needed.
 

 

Qualifications

•    Bachelor’s degree (Master’s or PhD preferred) in data science, statistics, applied analytics, science, engineering or related quantitatively rigorous field.
•    5 or more years of relevant experience with predictive analytics.  Energy industry experience a plus.
•    Experience forecasting using state-of-the-art modelling required.  Knowledge in time series, regression, machine learning and/or deep learning as applied to forecasting is required. Experience in energy load forecasting preferred. 
Strong written and verbal communication skills required, including ability to explain complex technical concepts to non-technical stakeholders. 
Python, pandas, and git experience, or its equivalent is required.  Expertise in R, Spark, advanced SQL skills, and advanced Excel preferred.  Experience with Databricks and Azure cloud products a plus. 
Experience understanding the short-term impacts of weather and distributed resources on loads is preferred.
 Knowledge of how to integrate short-term forecasting processes into day-to-day operational data engineering and production pipelines is a plus. 

 

More Information

Salary

$143,000 - $168,000 a year

This position has a career path which provides for advancement opportunities within and across bands as you develop and evolve in the position; gaining experience, expertise and acquiring and applying technical skills. Internal candidates will be assessed and provided offers against the minimum qualifications of this role and their individual experience.