Data Science Lead - R01570082

Data Science Lead

Job requirements: With at least 8 years of experience in technical product management, engineering management, or similar roles leading technical teams in AI/ML or data-driven product development Key Responsibilities: 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 Required Skills: 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) Preferred Skills: 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 Desired Qualifications: 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)