Expert Data Scientist

About the role:

As an Expert Data Scientist, become a part of a cross-functional development team engineering experiences of tomorrow. The Expert/Principal Data Scientist is a senior technical leader responsible for setting the standard for evaluation-driven development across the organization's AI/ML and analytics portfolio. This person defines modeling strategy, evaluation frameworks, and delivery governance across multiple 45-day release cycles, and is accountable for demonstrating measurable business impact at a portfolio level while mentoring senior and mid-level data scientists.

Responsibilities:

• Evaluation-Driven Development Strategy & Standards
• Define and champion the organization's evaluation-driven development framework: standards for selecting modeling approaches (statistical, ML, optimization, simulation, GenAI), evaluation metric design, and validation methodology
• Establish enterprise guidelines for aligning evaluation metrics to business and process outcomes, ensuring consistency across teams and products
• Set standards for the use of synthetic data, historical data, and real-world/controlled validation across the model lifecycle, including approval gates before production rollout
• Own the definition of "measurable improvement" for the portfolio — ensuring every release is backed by a rigorous before/after comparison against business KPIs
• Delivery Leadership
• Oversee and be accountable for multiple concurrent workstreams delivered through 45-day versioned agile cycles, ensuring the portfolio consistently ships incremental, measurable value
• Act as the escalation point for delivery risk, technical trade-offs, and cross-team dependencies across release cycles
• Partner with product and engineering leadership to prioritize the roadmap based on expected business impact vs. delivery cost/risk
• Platform, MLOps & Cost
• Set architectural and MLOps direction for AI/ML platforms and analytics products on AWS, including model deployment patterns, monitoring, and automated governance
• Own cost-management and performance-optimization strategy for data and ML workloads at scale, setting budgets and efficiency targets across teams
• Define enterprise approach to synthetic data generation, offline experimentation, and controlled real-world validation, including tooling selection
• Governance & Compliance
• Own the framework for balancing model performance, explainability, regulatory compliance, and cost across the portfolio, including escalation criteria for high-risk models
• Represent the data science function in audits, regulatory reviews, and executive reporting on model governance and business impact
• Leadership
• Mentor Senior Data Scientists and review their evaluation frameworks, modeling choices, and delivery outcomes
• Drive adoption of best practices in evaluation-driven development across the organization

Requirements:

• Master's or PhD in Data Science, Statistics, Computer Science, Operations Research, or related field (or equivalent demonstrated experience)
• 10+ years of experience delivering data science/ML/analytics solutions, including leadership of technical strategy at a portfolio or platform level
• Proven track record of selecting and justifying modeling approaches across statistical, ML, optimization, simulation, and GenAI paradigms based on business context
• Deep experience defining evaluation metrics tied to business and process KPIs, and demonstrating measurable outcomes from delivered models
• Extensive hands-on and architectural experience with AWS AI/ML platforms and MLOps practices at scale
• Demonstrated ownership of agile, versioned delivery cycles across multiple teams or products
• Strong track record balancing model performance, explainability, compliance, and cost trade-offs, including in regulated environments
• Experience designing synthetic data strategies, offline experimentation frameworks, and controlled real-world validation programs
• Excellent stakeholder management and executive communication skills

Desirable:

• Experience presenting model governance and impact metrics to executive or regulatory audiences
• Publications, patents, or public speaking in applied ML/AI evaluation methodology
• Experience building or scaling MLOps platforms from the ground up