Architect V - Data Science

This position is responsible for technology leadership, with responsibilities including the development of strategic application architecture frameworks and the envisioning, planning and development of technology roadmaps across a wide array of fields.

The Enterprise Architecture team is seeking a Principal Data Scientist to help define how the organization designs, governs, and scales data science and analytics capabilities across the enterprise. This is a hybrid strategist-practitioner role: you will architect enterprise-wide data science patterns and standards on our Snowflake-centered data platform while also leading the design and delivery of high-impact statistical and machine learning solutions.

You will partner closely with Enterprise Architects, Data Engineering, IT, and business stakeholders to ensure that advanced analytics capabilities are built on a scalable, governed, and reusable foundation — rather than one-off solutions.

This role is ideal for a senior data scientist who thinks in systems: someone who can move fluidly between building a production-grade model, designing a reusable feature engineering pattern in Snowflake, and advising architecture governance boards on the right way to operationalize AI/ML didate will have at least 10 years experience working for a company or governmental agency with at least 300 employees (if private sector) or 500 employees (if governmental agency).

• Define and evangelize enterprise standards, reference architectures, and reusable design patterns for data science, machine learning, and statistical modeling workflows built on Snowflake and Databricks.
• Partner with Enterprise Architecture leadership to align data science roadmaps with the broader enterprise data and technology strategy, including data governance, security, and platform modernization initiatives across both platforms.
• Serve as a technical authority in architecture review boards, evaluating proposed analytics and ML solutions for scalability, cost-efficiency, security, and reusability before they move to production.
• Design and champion MLOps and Model Ops patterns (versioning, monitoring, retraining, feature stores) that operationalize models reliably across Snowflake (Snowpark, Snowflake ML/Cortex, Snowflake Feature Store) and Databricks (MLflow, Unity Catalog, Feature Store, Databricks Model Serving).
• Define clear architectural guidance on when to leverage Snowflake versus Databricks for a given workload, and design interoperability patterns so data and models move cleanly between the two platforms.

• Design and build mathematical optimization models (linear/mixed-integer programming, heuristics, simulation) to solve enterprise planning problems such as network design, capacity planning, resource allocation, and inventory/demand optimization.
• Partner with business planning functions to modernize and scale planning processes — translating manual or spreadsheet-based planning into governed, data-driven optimization solutions running on Snowflake and Databricks.
• Evaluate and integrate optimization solvers and frameworks (e.g., Gurobi, CPLEX, OR-Tools, PuLP) into enterprise architecture patterns so planning solutions are reusable, auditable, and maintainable at scale.
• Combine forecasting and optimization techniques to support end-to-end planning cycles (e.g., demand planning feeding into network or capacity optimization).

• Lead end-to-end development of advanced statistical models, machine learning algorithms, and forecasting solutions that solve complex, ambiguous business problems with enterprise-wide impact.
• Translate business…