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)