The Role
Lead AI and data science teams across enablement and experimentation initiatives. Set priorities, translate business needs into technical work, manage operating rhythms and resources, guide AI technology roadmaps, evaluate experiments, establish responsible AI governance, and optimize infrastructure and LLM-related costs. The role requires advanced Python, LLM and RAG expertise, machine learning frameworks, Azure deployment experience, statistical and forecasting knowledge, and ML lifecycle management skills.
Summary Generated by Built In
Data Science Lead
Job requirements
- 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
- 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)
- 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
- 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)
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:
Required Skills:
Preferred Skills:
Desired Qualifications:
Skills Required
- 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
- Advanced proficiency in Python for code review, scripting, and prototyping
- Strong understanding of LLM-based systems, including retrieval-augmented generation pipelines
- Experience with TensorFlow, PyTorch, and Scikit-learn
- Hands-on experience with Microsoft Azure infrastructure for deploying, monitoring, and scaling AI workloads
- Expertise in statistical analysis and computing, including hypothesis testing, t-tests, z-tests, and regression techniques
- Proficiency in exponential smoothing, ARIMA, and ARIMAX forecasting techniques
- Knowledge of classification algorithms including decision trees and SVM
- Familiarity with KubeFlow and BentoML for ML lifecycle management
- Understanding of probabilistic graph models and Hamming, Euclidean, and Manhattan distance metrics
- Experience with agent orchestration patterns for multi-step AI workflows
- Expertise in prompt engineering for 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
- 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
Brillio Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Brillio and has not been reviewed or approved by Brillio.
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Healthcare Strength — Healthcare is considered comprehensive, including medical coverage for employees and dependents alongside life, disability, and accidental death protections. Feedback suggests these protections are a core strength of the package.
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Leave & Time Off Breadth — Time-off options include paid leave and parental leave, with flexible or ‘flexible PTO’ approaches cited in some contexts. Feedback suggests this breadth helps support work-life balance when team norms permit usage.
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Wellbeing & Lifestyle Benefits — Wellbeing offerings span counseling, financial-management sessions, fitness programs, and travel insurance, plus region-specific extras like discounted IT hardware and work-from-home essentials. Feedback suggests these add-ons enhance perceived value beyond core insurance.
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The Company
What We Do
Brillio is the leader in global digital business transformation, applying technology with a human touch. We help businesses define internal and external transformation objectives, and translate those objectives into actionable market strategies using proprietary technologies. With 2600+ experts and 13 offices worldwide, Brillio is the ideal partner for enterprises that want to quickly increase their core business productivity, and achieve a competitive edge, with the latest digital solutions.
