Job description

JOB SUMMARY 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.Candidate 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). ESSENTIAL DUTIES AND RESPONSIBILITIES Enterprise Data Science Architecture & Strategy • 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. Optimization & Planning Solutions • 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). Advanced Analytics & Model Development • 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. Governance, Mentorship & Collaboration • 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. JOB QUALIFICATIONS: Education & Experience 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. • Experience in the logistics, transportation, or supply chain domain (e.g., network optimization, fleet/route planning, warehouse or distribution planning) is a strong plus. JOB QUALIFICATIONS: Essential Skills, Abilities and Example Behavior(s) • CUSTOMER FOCUS: Provides service excellence to internal and/or external customers. Provides added value. Looks for ways to add value beyond clients' immediate requests. Addresses the unidentified, underlying and long-term client needs. Enhances client service delivery systems and processes. Anticipates clients' upcoming needs and concerns. • RESULTS ORIENTATION: Focuses efforts on achieving high quality results consistent with the organization’s standards. Exceeds standards. Defines ambitious, but realistic, personal goals and standards. Evaluates personal progress against standards. Undertakes significant challenges. Adjusts actions to meet and exceed expectations. Tries new ways to get things done, while taking steps to reduce the risks. • TEAMWORK / COLLABORATION: Works collaboratively with others to achieve organizational goals. Proactively assists and involves others. Initiates collaboration with others. Assumes additional responsibilities to facilitate the achievement of team goals. Seeks input from other team members on matters that affect them. • HIGH STANDARDS: Follows up on procedures, ensures high quality output, takes action to solve quality problems or notify quality issues as appropriate. Exceeds quality standards set by organization. Follows all the policies and procedures for quality set by the organization. Works at a level that consistently meets and often exceeds quality standards set by the organization. Seeks opportunities to improve how work is done, raising the quality of the output. • BUSINESS PERSPECTIVE: Uses an understanding of business issues, processes and outcomes to enhance business performance. Makes recommendations to improve business operations. Offers concrete suggestions to reduce costs, improve quality or revenue for aspects of key products or services in own area. Identifies potential new clients for own area. Recognizes the value of all major business areas, avoiding a "single area" bias. • CREATIVITY AND INNOVATION: Generates viable, new approaches and solutions. Modifies current approaches. Modifies current approach to better meet needs. Thinks about problems from a new perspective. Expands on the thinking or solutions proposed by others. Demonstrates curiosity, digging for information below the surface. • FOSTERING COMMUNICATION: Listens and communicates openly, honestly, and respectfully with different audiences, promoting dialogue and building consensus. Fosters two-way communication. Recalls others' main points, taking them into account in own communication. Checks own understanding of others' communication (e.g., paraphrases, asks questions). Elicits comments or feedback on what has been said. Maintains continuous, open and consistent communication with others. • INITIATIVE: Deals with situations and issues proactively and persistently, seizing opportunities that arise. Addresses current issues. Acts on issues in own area of responsibility instead of waiting or hoping the problem will solve itself. Tries varied approaches and solutions to resolve a problem. Persists when significant difficulties arise. • PROBLEM SOLVING: Identifies problems and the solutions to them. Solves standard problems. Identifies standard problems based on a range of factors, most of which are clear. Identifies alternate solutions, considering applicable precedents. Identifies optimal solutions based on weighing the advantages and disadvantages of alternative approaches. Evaluates the effectiveness and efficiency of solutions after implementation. • TECHNICAL CAPABILITY: Understands and applies functional and technical knowledge and skills to accomplish work objectives. Keeping up to date with new development in the subject area and continuing to enhance the skills. Applies technical knowledge and skills in a range of typical situations, with minimal or no guidance. Follows standard procedures, where applicable, for common tasks. Seeks support from senior staff when encountering atypical issues. • SYSTEM DESIGN: Identifying and selecting system options which are balanced and optimized and applying techniques to a variety of system design contexts to ensure system interoperability, scale, performance, usability, and security. Demonstrates solid knowledge and ability, and can apply the competency, with minimal or no guidance, in the full range of typical situations. Would require guidance to handle novel or more complex situations. Maintains control over the system design life cycle. Analyzes architecture business requirements and prepares system design proposals. Provides support for problem resolution activities. Conducts impact analysis for proposed changes to the system. Provide system design guidance to the team. • SYSTEMS INTEGRATION AND PORTING: The integration and testing of software products, components and/or subsystems and their interfaces into existing, new or modified software and hardware platform environments to deliver automated solutions according to business and technical specifications. Demonstrates solid knowledge and ability, and can apply the competency, with minimal or no guidance, in the full range of typical situations. Would require guidance to handle novel or more complex situations. Produces integrated software builds using complex programming and software modules. Configures the hardware environment, produces integration test specifications, and conducts tests as required. Verifies that the logical design components are consistent with integration target architecture requirements. Carries out support and production assurance for common systems integration problem resolution activities. • INFRASTRUCTURE/PLATFORMS: Supporting the enterprise computing infrastructure (e.g., enterprise servers, client server, storage devices and systems, hardware and software) in the provision, management, storage, operation, scheduling, support and maintenance of the infrastructure. Demonstrates solid knowledge and ability, and can apply the competency, with minimal or no guidance, in the full range of typical situations. Would require guidance to handle novel or more complex situations. Describes how the platform integrates with other enterprise elements. Uses performance data collection tools and techniques to mitigate down-time and improve performance. Installs software and hardware on the platform. Solves routine hardware and software problems, documenting new problems and their solution for future reference. Uses appropriate diagnostic tools to solve complex problems. Executes standards without supervision, suggesting modifications to these standards. • PROCESSES, METHODOLOGIES, AND TOOLS: The development of expertise and the provision of advice, assistance and leadership in processes, methodologies and tools used in any specific area of technology by focusing on definition, management, repeatability and measurement to ensure appropriate and effective use throughout the organization. Demonstrates basic knowledge and ability and, with guidance, can apply the competency in common situations that present limited difficulties. Selects appropriate tools and methodologies to deliver applications and systems that meet defined organizational business needs. Works with the QA Team to contribute to the continual improvement of QA standards and best practices. Reviews all integration components to ensure that source code control practices are followed. Captures functional and nonfunctional requirements. Develops business analysis practices and standards, as well as associated tools, templates and techniques. Reviews and identifies modifications to existing software development/integration process approaches. Develops new ways to achieve process improvements. Identifies where software process, methodologies and/or tools would increase the IT organization capacity to contribute to meeting business needs. Evaluates the effectiveness of development and integration processes, methodologies and tools used in application systems development activities.

Requirements

Must-haves

Enterprise Data Science Architecture & Strategy
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.
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.
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.
Optimization & Planning Solutions
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).
Advanced Analytics & Model Development
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.
Governance, Mentorship & Collaboration
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.
JOB QUALIFICATIONS: Education & Experience 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.
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.).

Nice-to-haves

Experience with cloud platforms; Azure strongly preferred, along with modern data/ML tooling (dbt, Airflow, MLflow, Docker/Kubernetes).

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaimlsqlpythonazuredockerkubernetes

Platform: ukg
Effort: medium

Benefits

Benefits detected in the listing.

Vision