Data Scientist (Advanced Metering Infrastructure - AMI)

Job Ttitle: Data Scientist (Advanced Metering Infrastructure - AMI)
Section: AMI & Meter Recording
Job Purpose:
Lead advanced analytics, machine learning, and predictive modelling initiatives using AMI data. Support strategic and operational decision-making, system optimization, and performance enhancement within the utility environment.
Core Responsibilities
* Design, develop, and deploy predictive and prescriptive analytics models leveraging AMI data.
* Build algorithms to identify consumption patterns, anomalies, losses, and system inefficiencies.
* Apply advanced statistical, machine learning, and AI techniques to support AMI performance teams.
* Proactively identify data integrity, performance, and system risks, and recommend preventive actions.
* Translate complex analytical results into actionable business and operational insights.
* Support the deployment of models into production environments and MLOps workflows.
* Mentor analysts and engineers on advanced analytics methodologies.
* Ensure best practices in data governance, model validation, and ethical AI usage.
* Support strategic initiatives related to smart grids and digital transformation.
* Perform other duties as assigned by senior management.
Education, Experience & Skills
Qualifications / Certifications
* PhD in Data Science, AI, Computer Science, or a related field; alternatively, a Master's degree with a strong research background.
* Certification in advanced Data Science, Machine Learning, or Artificial Intelligence.
* Demonstrated research or professional experience in predictive/prescriptive analytics and ML model deployment (MLOps).
Experience
* Minimum 13+ years of professional experience in Data Science or Advanced Analytics.
* Experience in the utilities or energy sector is preferred.
Key Skills
* Advanced Machine Learning, statistical modelling, and data architecture.
* Strong programming proficiency in Python and Java.
* Proven experience delivering end-to-end analytics solutions in production environments.