Principal Data Scientist, Snowflake,Databricks, AI 100% Remote Permanent Job

Zeektek; United States; Hybrid; USD 180,000 - 240,000; Full time

Job summary

Zeektek seeks a Principal Data Scientist to define enterprise data science standards on a Snowflake-centered platform. You will architect reusable patterns and lead MLOps, model lifecycle, and governance across the organization.

You will mentor teams, collaborate with IT, data engineering, and stakeholders, and advance analytics capabilities at scale with cloud-native tools and AI/ML methods.

Qualifications

• Master’s or PhD in a quantitative field and 10+ years in data science/analytics.
• Hands-on expertise with Snowflake, Snowpark, Cortex, and MLflow at enterprise scale.
• Strong Python/R, Spark, ML/DL frameworks, and statistical methods.
• Experience leading enterprise architecture patterns and governance.
• Proven ability to translate business questions into analytics solutions.

Responsibilities

• Define enterprise data science standards and reusable patterns on Snowflake/Databricks.
• Lead design of MLOps patterns, feature stores, and model lifecycle.
• Guide platform choices and ensure secure, scalable interoperability.
• Develop optimization, forecasting, and prescriptive analytics for planning.
• Mentor data scientists and analytics engineers; drive best practices.

Skills

Enterprise architecture

Data science strategy

ML model development

Mentorship

Communication to executives

A/B testing & causal inference

Production ML lifecycle

Forecasting & optimization

Python / R analytics

Spark / PySpark

Cloud & data platforms

Education

Master’s degree

PhD (preferred)

Tools

Snowflake

Databricks

Snowpark

Cortex

MLflow

Unity Catalog

Spark/PySpark

Python

R

pandas / scikit-learn

PyTorch / TensorFlow

Gurobi / CPLEX / OR-Tools / PuLP

dbt / Airflow

Azure

Docker / Kubernetes

Job description

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 & StrategyDefine enterprise standards, reference architectures, and reusable patterns for data science and ML on Snowflake and Databricks, aligned with enterprise data, governance, and security strategy.
• Serve as technical authority on architecture review boards, evaluating ML and analytics solutions for scalability, cost, security, and reuse.
• Design MLOps/ModelOps patterns (versioning, monitoring, retraining, feature stores) using Snowpark, Snowflake ML/Cortex, MLflow, Unity Catalog, and Databricks Model Serving.
• Guide platform selection between Snowflake and Databricks and design interoperability between them.
• Optimization & Planning SolutionsBuild optimization models (LP/MIP, heuristics, simulation) for network design, capacity planning, resource allocation, and inventory/demand optimization.
• Modernize spreadsheet-based planning into governed, data-driven solutions, integrating solvers such as Gurobi, CPLEX, OR-Tools, and PuLP.
• Combine forecasting and optimization to support end-to-end planning cycles.
• Advanced Analytics & Model DevelopmentLead development of statistical, ML, and forecasting models for complex, enterprise-wide business problems.
• Translate executive and stakeholder questions into analytical approaches and communicate actionable recommendations.
• Perform EDA, feature engineering, and model validation on large datasets; build models with Spark/PySpark and manage the lifecycle in MLflow.
• Validate model impact through A/B testing, causal inference, and quasi-experimental design.
• Governance, Mentorship & CollaborationSet best practices for data quality, documentation, reproducibility, and responsible AI.
• Mentor data scientists and analytics engineers to raise the team’s technical bar.
• Partner with Data Engineering, Cloud, IT Security, and Enterprise Architecture to keep solutions secure and interoperable.
• Track emerging capabilities (Cortex AI, Snowpark Container Services, Mosaic AI) and recommend adoption where valuable.

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.