Architect V - Data Science

PODS•Clearwater, FL

2d•Hybrid

About The Position

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 at scale.

Requirements

Master’s degree or PhD in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
10+ years of professional experience in data science, applied machine learning, or advanced analytics, including experience operating at a senior/principal level with enterprise-wide scope.
Hands-on, must-have expertise with Snowflake — including writing and optimizing complex Snowflake SQL, working with Snowpark (Python), designing scalable data models and pipelines, and leveraging Snowflake's native ML/AI capabilities (Cortex, Feature Store, ML Functions).
Hands-on, must-have expertise with Databricks — including Spark/PySpark for large-scale data processing, the Lakehouse architecture (Delta Lake), MLflow for experiment tracking and model lifecycle management, and Unity Catalog for governance.
Strong programming skills in Python and/or R, with deep experience in the modern data science stack (pandas, scikit-learn, PyTorch/TensorFlow, Spark MLlib, etc.).
Solid foundation in statistics and machine learning techniques: regression, classification, clustering, time-series forecasting, and experimentation/causal inference.
Hands-on experience with mathematical/prescriptive optimization (linear programming, mixed-integer programming, or heuristic/simulation-based approaches) applied to planning or resource-allocation problems.
Demonstrated experience designing or contributing to enterprise architecture patterns — data architecture, solution architecture, or ML/MLOps architecture — in a large, matrixed organization.
Experience with cloud platforms; Azure strongly preferred, along with modern data/ML tooling (dbt, Airflow, MLflow, Docker/Kubernetes).
Excellent communication skills, with a track record of translating complex technical concepts for both technical and executive, non-technical audiences.
6+ years developing production-quality solutions using Python, SQL, and Spark, including testing, version control, and software engineering best practices.
4+ years building scalable data pipelines, managing data quality at scale, and supporting cloud-based data lake architectures, including 3+ years with Databricks or a comparable modern data and AI platform.
2+ years designing and deploying Generative AI solutions, including Large Language Models (LLMs), AI agents, and related AI-powered applications within enterprise environments.
3+ years leading or mentoring data scientists and engineers, conducting technical design and code reviews, building analytics and visualization solutions, and translating complex business challenges into machine learning, analytics, and AI products.
Experience with commercial or open-source optimization solvers (Gurobi, CPLEX, OR-Tools) and/or supply chain planning platforms (e.g., o9, Kinaxis, Blue Yonder, SAP IBP).
Experience with enterprise architecture frameworks (e.g., TOGAF) or formal participation in architecture governance/review processes.
Familiarity with data governance and cataloging tools (e.g., Collibra, Alation) and modern orchestration/transformation tools (dbt, Airflow).
Experience building and scaling internal ML platforms, feature stores, or self-service analytics capabilities.

Nice To Haves

Experience in the logistics, transportation, or supply chain domain (e.g., network optimization, fleet/route planning, warehouse or distribution planning) is a strong plus.

Responsibilities

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 ModelOps 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 (Spark/PySpark), and manage the model lifecycle end-to-end with MLflow.
Apply rigorous experimentation methods (A/B testing, causal inference, quasi-experimental design) to validate model impact and support data-driven decision-making.
Establish and enforce best practices for data quality, model documentation, reproducibility, and responsible/ethical AI across the data science function.
Mentor and provide technical guidance to data scientists and analytics engineers; act as a force multiplier by raising the technical bar across the team.
Collaborate closely with Data Engineering, Platform/Cloud Engineering, IT Security, and Enterprise Architecture peers to ensure data science solutions are secure, well-governed, and interoperable with enterprise systems.
Stay current on emerging data science, AI/ML, and platform capabilities (e.g., Snowflake Cortex AI, Snowpark Container Services, Databricks Mosaic AI) and proactively recommend adoption where it creates business value.