Peter Millar
Senior Data Scientist
Research Triangle Park, NC · Senior
Sponsorship not specifiedDetected 30 days ago
PythonSQLAzureCloud PlatformsMachine LearningData EngineeringData ScienceData VisualizationNLPLLMsRAGAgentic AIA/B TestingForecastingResearchLeadershipCommunicationCollaborationMentoringDemand Planning
About the role
- It's fun to work in a company where people truly BELIEVE in what they're doing!
- We're committed to bringing passion and customer focus to the business.
- The Senior Data Scientist expands Peter Millar's data science capacity beyond customer analytics, owning forecasting, merchandising, and operations modeling.
Nice to have
- Microsoft Fabric - hands-on experience deploying models in the Fabric Data Science workload (notebooks, MLflow, model registry).
- OneLake. experience accessing and engineering features from OneLake (Lakehouse, Delta tables, shortcuts).
- Programming. strong Python and SQL with production-level coding experience.
- Experience with BI tools (Power BI preferred) and exposure to GenAI/NLP use cases is a plus.
- Strong proficiency in Python and SQL with production-level coding experience.
- Hands-on experience deploying models in cloud environments (Microsoft Fabric/Azure preferred).
- Strong foundation in statistical methods including regression, classification, clustering, and time series.
- Experience translating analytical outputs into business insights for non-technical stakeholders.
Skills
- Establish and enforce best practices for code quality, version control, model documentation, and reproducibility.
- Translate ambiguous business problems into well-scoped analytical projects with clear deliverables.
- Foster a culture of continuous learning, feedback, and technical excellence.
- Present complex technical findings to non-technical audiences in a clear and compelling manner.
- Leverage external and group-level resources to enhance analytical capabilities and benchmarks.
- Conduct deep-dive analyses combining internal and external data sources to generate actionable consumer insights.
- Stay current on advancements in machine learning, AI, and consumer analytics.
- Establish guardrails for responsible AI usage, including validation, explainability, and cost management.
- Identify and scale high-value AI use cases across the business.
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