Data Science Lead
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 goalsServe as the primary liaison with internal business teams to understand workflows, gather requirements, and translate business pain points into actionable technical workCollaborate with product teams to align exploration and experimentation efforts with broader product directionLead the team’s operating rhythm, including stand-ups, demos, planning sessions, and progress readouts to leadership and stakeholdersAllocate resources across workstreams, moving team members based on shifting priorities to maximize impact and efficiencyEvaluate and shut down experiments or projects that are not delivering results, reprioritizing efforts swiftly and effectivelyGuide the team’s technology roadmap by making decisions on model selection, infrastructure, build-vs-buy tradeoffs, and adoption of new toolsDefine and evolve AI governance and compliance practices, establishing guardrails for responsible AI use, data handling, and decision explainabilityManage and optimize AI infrastructure spend, tracking LLM costs, token usage patterns, and vendor contracts to ensure cost-effective operationsRequired Skills:
Advanced proficiency in Python for code review, scripting, and prototypingStrong understanding of LLM-based systems, including retrieval-augmented generation pipelinesExperience with ML frameworks such as TensorFlow, PyTorch, and Sci-Kit LearnHands-on experience with Azure cloud infrastructure for deploying, monitoring, and scaling AI workloadsExpertise in statistical analysis and computing, including hypothesis testing, t-test, z-test, and regression techniquesProficiency in forecasting techniques including exponential smoothing, ARIMA, and ARIMAXKnowledge of classification algorithms such as decision trees and SVMFamiliarity with tools like KubeFlow and BentoML for ML lifecycle managementUnderstanding of probabilistic graph models and advanced distance metrics (Hamming, Euclidean, Manhattan)Preferred Skills:
Experience with agent orchestration patterns for multi-step AI workflowsExpertise in prompt engineering to optimize output quality in LLM-based systemsProficiency with Great Expectations and Evidently AI for data validation and monitoringExperience defining AI governance frameworks for compliance and responsible data handlingDesired Qualifications:
Bachelor's degree in Computer Science, Data Science, Statistics, Information Technology, or a closely related disciplineCertification in Machine Learning, Data Science, or Artificial Intelligence from a recognized institutionCertification in Azure AI or Cloud Services (such as Microsoft Certified: Azure AI Engineer Associate)
