The Senior Data Scientist expands Peter Millar's data science capacity beyond customer analytics, owning forecasting, merchandising, and operations modeling. Operating at the intersection of applied machine learning, business strategy, and the modern data platform (Microsoft Fabric, OneLake, and Azure), this role designs, builds, and deploys predictive models that have direct revenue impact, partners with data engineering to productionize workflows, and helps advance the organization's AI-driven capabilities. The role is a technical peer to the Lead Data Engineer and provides succession depth for senior data science work.
This is a hybrid position located in Durham, NC, with the expectation of regular on-site work across both our Durham and Raleigh offices.ESSENTIAL FUNCTIONS:
Technical Leadership & Execution
- Design, build, and deploy predictive models and machine learning solutions across use cases such as customer segmentation, demand forecasting, lifetime value, and personalization.
- Establish and enforce best practices for code quality, version control, model documentation, and reproducibility.
- Collaborate with data engineering to architect scalable data pipelines and modeling workflows within Microsoft Fabric and Azure.
- Evaluate and introduce new tools, frameworks, and methodologies to advance applied data science capabilities.Data Visualization & Self-Service Analytics
Mentorship & Team Development
- Act as the primary technical mentor for junior data scientists, providing guidance on modeling approaches, code quality, and analytical rigor.
- Translate ambiguous business problems into well-scoped analytical projects with clear deliverables.
- Foster a culture of continuous learning, feedback, and technical excellence.
- Partner with leadership to identify skill gaps and development plans.
Cross-Functional Collaboration & Communication
- Partner with business stakeholders across Marketing, Merchandising, Retail, and E-commerce to translate analysis into actionable insights.
- Present complex technical findings to non-technical audiences in a clear and compelling manner.
- Collaborate with IT and data engineering teams to ensure reliable access to data and infrastructure.
- Leverage external and group-level resources to enhance analytical capabilities and benchmarks.
Insight Generation & Applied Research
- Conduct deep-dive analyses combining internal and external data sources to generate actionable consumer insights.
- Design and analyze experiments (A/B testing, multivariate testing) to measure business impact.
- Stay current on advancements in machine learning, AI, and consumer analytics.
AI-Enabled Analytics & Agent Development
- Design and deploy AI agents and LLM-powered tools to automate analytics workflows.
- Build integrations using Azure services, including APIs and function apps.
- Establish guardrails for responsible AI usage, including validation, explainability, and cost management.
- Identify and scale high-value AI use cases across the business.
Technical Competencies (Preferred):
- 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).
- Azure AI - production experience with Azure Machine Learning and Azure AI services.
- Microsoft Foundry (Azure AI Foundry) - experience building GenAI/agentic solutions with the model catalog, RAG, and Foundry Agent Service (applicable to this role).
- 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.
DESIRED EDUCATION AND EXPERIENCE:- 5-8+ years of experience in data science, machine learning, or advanced analytics.
- 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.
- Experience mentoring or leading junior team members.
- Strong communication skills and ability to influence cross-functional partners.
- Master's degree in a quantitative field or equivalent experience.
