Systems Manager - Enterprise Data Science and AI

The Systems Manager, Enterprise Data Science and AI, leads the development and application of advanced analytics, statistical modeling, machine learning, and agent-enabled AI to deliver actionable insights, predictions, and decision support across Con Edison's grid operations, asset management, forecasting, customer analytics, and enterprise planning functions. The role uses Google Vertex AI as the primary platform for model development, orchestration, and lifecycle management, including agentic AI capabilities. This position ensures that predictive models and agent-based workflows are secure, explainable, auditable, and aligned with enterprise governance standards, especially when AI driven outputs influence operational or customer facing decisions.Operating in a mission critical, regulated utility environment, the Systems Manager, Enterprise Data Science and AI, translates business and operational needs into well-defined data science and modeling requirements, working closely with Data Engineering, AI Platform, and Data Quality Assurance teams. The role is accountable for data quality validation, feature consistency, model performance, and ongoing monitoring for drift and anomalies. In addition to owning the end-to-end data science and agent lifecycle, the position serves as a bridge between advanced AI capabilities and real-world operational adoption, ensuring solutions deliver measurable value while meeting Con Edison's non-negotiable standards for safety, compliance, and operational resilience. Core Responsibilities

• Lead data science efforts to generate insights, predictions, and decision support across enterprise and operational use cases
• Translate business and operational needs into analytical, statistical, and modeling requirements
• Specify data and feature requirements needed to support modeling and analysis
• Consume curated, analytics-ready and feature-ready datasets provided by data engineering teams
• Validate the statistical soundness and suitability of datasets for analysis and modeling
• Use enterprise metadata and data definitions to understand data meaning, scope, and limitations
• Leverage mastered and reference data to ensure consistency across analyses and models
• Identify and flag data quality issues that may impact analytical accuracy or model performance
• Request and maintain appropriate data access in alignment with security and governance policies
• Design features and transformations required for predictive and machine learning models
• Develop, train, test, and evaluate statistical, machine learning, and AI models
• Support model deployment in partnership with AI platform and engineering teams
• Monitor model performance, data drift, and input anomalies in production environments
• Document models, assumptions, and limitations to support transparency and auditability
• Collaborate with governance, engineering, and platform teams to ensure compliance with regulatory and enterprise standards
• Lead and develop a team of managers through coaching, performance management, and effective work assignment to drive aligned execution and business outcomes

Required Education/Experience

• Master's Degree and a minimum of 6 years full-time relevant work experience or
• Bachelor's Degree and a minimum of 8 years full-time relevant work experience.

Preferred Education/Experience

• Master's Degree in Business Administration, Finance, Accounting, Management Information Systems, Information Systems, related business or technology aligned field and a minimum of 6 years full-time relevant work experience.

Relevant Work Experience

• Demonstrated experience leading enterprise data science and AI teams delivering advanced analytics, statistical models, machine learning solutions, and agent enabled AI capabilities in regulated environments, preferred
• Proven hands-on experience using Google Vertex AI for model development, orchestration, deployment, and lifecycle management, including support for agent-based workflows, preferred
• Strong background translating complex business and operational needs into well-defined data science, modeling, and analytical requirements, preferred
• Demonstrated experience developing predictive and prescriptive models that support grid operations, asset management, forecasting, customer analytics, and enterprise planning, preferred
• Proven ability to design and operate secure, explainable, auditable, and compliant AI solutions, especially when outputs influence operational or customer facing decisions, preferred
• Experience owning the full data science and agent lifecycle, including data exploration, feature engineering, model training, validation, deployment, monitoring, and continuous improvement, preferred
• Strong expertise in data quality va