About the role
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)
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