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.

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 questions from executive and cross-functional stakeholders into well-scoped analytical and modeling approaches, and communicate findings and recommendations in clear, actionable terms.
• Perform advanced exploratory data analysis, feature engineering, and model validation directly against large-scale datasets in Snowflake and Databricks, optimizing for performance and cost.
• Build and train machine learning models using Databricks notebooks and distributed compute…