Senior Data Scientist

Essential Functions

• 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.