AI Value Governance & Insights Lead (Hybrid)

Morgan Stanley • Montreal, QC

11d • Hybrid

About The Position

We are seeking an AI Value Governance & Insights Lead to join the Enterprise Architecture and Modernization fleet in a pivotal role focused on building and scaling the firm’s AI cost and value management capabilities. This role combines advanced AI with data and analytics capabilities to develop scalable frameworks for AI cost management, forecasting, and optimization, delivering clear business value through a centralized platform that provides end-to-end visibility into AI investments. It also provides strategic leadership to a growing Montreal-based team, ensuring alignment between global priorities and regional execution while driving strong stakeholder engagement and end-to-end delivery. The Enterprise Architecture and Modernization (EAM) team drives the firm’s technology strategy by establishing scalable, resilient, and future-ready architectures aligned with business objectives, risk, and regulatory requirements. They enable modernization and innovation by defining standards, governance frameworks, and advancing capabilities across cloud, data, distributed systems, and AI. Through cross-functional collaboration, the team accelerates transformation, optimizes technology investments, and improves efficiency, while fostering a strong global architecture community focused on best practices, continuous learning, and long-term value creation. In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities. This is a Data & Analytics Engineering position at Vice President level, which is part of the job family responsible for providing specialist data analysis and expertise that drive decision-making and business insights as well as crafting data pipelines, implementing data models, and optimizing data processes for improved data accuracy and accessibility, including applying machine learning and AI-based techniques Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world.

Requirements

• 10+ years of experience in data analytics, data engineering, data science, or AI related field, with a strong focus on financial and cost analytics, including forecasting, run-rate modelling, variance analysis, and scenario-based simulations
• Strong Python expertise for data manipulation and advanced analytics (Pandas, NumPy, SciPy, PySpark), including AI/ML and statistical modelling (time series techniques, regression, predictive models)
• Proficiency in SQL and experience with large-scale cloud data platforms (e.g., Snowflake, Databricks, Dataiku)
• Strong understanding of data architecture, data modelling, and data warehousing concepts (dimensional modelling, Star Schema, Kimball methodology)
• Exposure to AI/LLM tokenomics, including analyzing token consumption, cost drivers, and optimization opportunities (cost per inference, efficiency metrics)
• Hands-on experience with data pipelines and processing frameworks (e.g., Apache Spark, Airflow) and ETL/ELT tools
• Experience building analytical dashboards (Power BI, Tableau), working with cloud platforms and cost models (Azure, AWS, GCP), and streaming and distributed systems (Kafka, RabbitMQ) alongside version control (Git).
• Strong analytical thinking and problem-solving skills, with the ability to translate complex data into actionable insights and effectively communicate and collaborate with technical and business stakeholders across global teams.
• Knowledge of French and English is required.

Responsibilities

• Define and implement data-driven approaches to align AI spend with measurable business outcomes (e.g., efficiency gains, time savings, revenue impact), enabling comprehensive cost-to-value analysis
• Drive AI spend forecasting and financial analytics using advanced analytics and machine learning, including run-rate modelling, variance analysis, and scenario-based simulations to improve cost visibility, identify key drivers, and support optimization efforts
• Integrate AI usage telemetry data to standardize tokenomics and usage tracking and develop KPI-based cost models that measure efficiency, business impact, and value generated across AI workloads.
• Design the scalable self-service analytics platforms to provide end-to-end visibility into AI usage, cost drivers, and consumption patterns across the firm
• Provide local leadership and strategic oversight to a small engineering team, ensuring alignment with global EAM initiatives and driving locally high-quality execution and delivery standards
• Collaborate with cross-functional stakeholders (technology, finance, legal, data protection, and business teams) to gather requirements, drive governance, and ensure compliance with enterprise policies and regulatory standards
• Proactively evaluate emerging technologies (e.g., Generative AI, Agentic AI) to identify opportunities for innovation, efficiency, and cost optimization for business
• Drive documentation, knowledge sharing, and thought leadership across AI, data engineering, and architecture communities

Benefits

• Ample opportunity to move about the business for those who show passion and grit in their work.
• Some of the most attractive and comprehensive employee benefits and perks in the industry.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager
• Education Level: No Education Listed