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)