Job Overview

The Senior Data Science Manager will lead the organization’s Data Science, AI/ML, MLOps, and model productionalization capabilities. This role is responsible for enabling internal teams and external AI teams to move machine learning models from experimentation into reliable, scalable, observable, and supported production environments.

The ideal candidate is a strong technical and people leader who can combine data science expertise, MLOps discipline, cloud platform knowledge, and business partnership to deliver world-class AI/ML services. This role will work closely with Data Engineering, Product Owners, Project Managers, business stakeholders, and the AI PMX lead to ensure a strong pipeline of AI/ML work, effective project discovery and estimation, and successful production delivery of high-value solutions.

Responsibilities:

• Own the organization’s Data Science, AI/ML, MLOps, and model productionalization capabilities, ensuring models are delivered with scalability, reliability, maintainability, and measurable business value and supported throughout their lifecycle.
• Lead and develop a high-performing team of data scientists, machine learning engineers, and AI/ML practitioners, including coaching, prioritization, performance management, hiring, and career development.
• Establish and mature a standardized MLOps framework across AWS and Databricks, including model development practices, deployment patterns, CI/CD, versioning, monitoring, governance, and operational support.
• Enable the productionalization of models developed by internal teams as well as models developed by external AI teams, ensuring solutions meet enterprise standards for security, performance, observability, supportability, and long-term maintainability.
• Develop and maintain AI/ML production observability capabilities, including model performance monitoring, drift detection, data quality monitoring, service health, usage tracking, alerting, and operational dashboards.
• Create and manage a support system for productionalized AI/ML models, including incident response, issue triage, ownership models, service-level expectations, runbooks, escalation paths, and continuous improvement processes.
• Partner closely with Data Engineers to ensure AI/ML solutions are built on reliable, scalable, and well-governed data foundations.
• Work closely with Product Owners and Project Managers to deliver world-class AI/ML services, ensuring clear scope, delivery plans, timelines, dependencies, risks, and success measures.
• Collaborate with the AI PMX lead to maintain a continuous flow of prioritized AI/ML projects, ensuring the team has a healthy pipeline of work planned at least one quarter into the future.
• Lead or support discovery and estimation of AI/ML projects, including opportunity assessment, feasibility analysis, business value sizing, effort estimation, technical complexity assessment, and delivery planning.
• Partner with business leaders and stakeholders to identify, prioritize, and deliver AI/ML use cases that create measurable value across the organization.
• Own the end-to-end delivery lifecycle for AI/ML solutions, from problem framing and discovery through experimentation, production deployment, adoption, monitoring, and support.
• Establish best practices for responsible AI, model governance, documentation, reproducibility, testing, validation, and compliance.
• Translate complex technical concepts into clear business recommendations, executive-ready narratives, and actionable delivery plans.
• Evaluate emerging AI/ML, MLOps, and generative AI technologies and determine practical applications that improve delivery speed, quality, scalability, and business impact.
• Define and track success metrics for AI/ML initiatives, including delivery performance, production reliability, model adoption, business value, and operational maturity.

Candidate Desired Profile:

Competencies