Data Science Lead - R01570082

Job requirements

• Set strategic priorities and determine team focus across AI Enablement and AI Experiments tracks, ensuring measurable progress toward organizational goals
• Serve as the primary liaison with internal business teams to understand workflows, gather requirements, and translate business pain points into actionable technical work
• Collaborate with product teams to align exploration and experimentation efforts with broader product direction
• Lead the team’s operating rhythm, including stand-ups, demos, planning sessions, and progress readouts to leadership and stakeholders
• Allocate resources across workstreams, moving team members based on shifting priorities to maximize impact and efficiency
• Evaluate and shut down experiments or projects that are not delivering results, reprioritizing efforts swiftly and effectively
• Guide the team’s technology roadmap by making decisions on model selection, infrastructure, build-vs-buy tradeoffs, and adoption of new tools
• Define and evolve AI governance and compliance practices, establishing guardrails for responsible AI use, data handling, and decision explainability
• Manage and optimize AI infrastructure spend, tracking LLM costs, token usage patterns, and vendor contracts to ensure cost-effective operations

• Advanced proficiency in Python for code review, scripting, and prototyping
• Strong understanding of LLM-based systems, including retrieval-augmented generation pipelines
• Experience with ML frameworks such as TensorFlow, PyTorch, and Sci-Kit Learn
• Hands-on experience with Azure cloud infrastructure for deploying, monitoring, and scaling AI workloads
• Expertise in statistical analysis and computing, including hypothesis testing, t-test, z-test, and regression techniques
• Proficiency in forecasting techniques including exponential smoothing, ARIMA, and ARIMAX
• Knowledge of classification algorithms such as decision trees and SVM
• Familiarity with tools like KubeFlow and BentoML for ML lifecycle management
• Understanding of probabilistic graph models and advanced distance metrics (Hamming, Euclidean, Manhattan)

• Experience with agent orchestration patterns for multi-step AI workflows
• Expertise in prompt engineering to optimize output quality in LLM-based systems
• Proficiency with Great Expectations and Evidently AI for data validation and monitoring
• Experience defining AI governance frameworks for compliance and responsible data handling

• Bachelor's degree in Computer Science, Data Science, Statistics, Information Technology, or a closely related discipline
• Certification in Machine Learning, Data Science, or Artificial Intelligence from a recognized institution
• Certification in Azure AI or Cloud Services (such as Microsoft Certified: Azure AI Engineer Associate)