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)
Skills
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