Senior Manager, ML Operations

Role Overview

• Manage a machine learning operations function responsible for ensuring rapid, low-risk launches, reliable real-time inference and training data pipelines, and strong monitoring/incident response for data science models and decisioning services
• Work closely with Data Science & Decision Science teams in enabling enterprise capabilities that create personalization/next best action at scale for Workplace plan participants and Personal Wealth clients
• Manage an agile, high-output team responsible for production deployment and operations of ML models and decisioning services
• Establish the decisioning “traditionalization pathway” aligned to center of excellence (COE) standards: from trained model artifacts to governed, monitored production releases
• Own execution across multiple data science and decision initiatives, ensuring delivery predictability, quality gates, and clear operational readiness
• Implement/oversee the implementation of reproducible training/scoring workflows using enterprise data foundations, in partnership with Data Science & Decision Science teams
• Build/operate feature pipelines and serving integrations on top of COE-owned data and cloud data platforms (e.g., Snowflake, Redshift) encompassing curated tables, governed access, and shared compute patterns
• Ensure consistent documentation, data lineage, auditability, and compliance with enterprise data governance
• Operationalize CI/CD for model releases using COE-approved toolchains and patterns (testing, packaging, artifact promotion, environment parity)
• Execute controlled pilots and rollouts in partnership with Decision Science for decisioning services, ensuring: a) clear launch criteria and success metrics defined with Data Science & Decision Science teams, b) automated smoke tests and validation, and c) rapid rollback mechanisms and runbooks
• Partner with the COE to standardize and improve these patterns for enterprise reuse
• Own decisioning inference integration patterns: real-time endpoints, routing, caching (when appropriate), and SLA-driven performance tuning
• Collaborate with Decision Science and Technology to enable policy/ranking updates and experimentation hooks (traffic splits, exposure logging, assignment consistency)
• Ensure the decisioning inference layer is production-grade in partnership with the COE: authenticated, secure, scalable, and observable
• Craft, implement and maintain monitoring for decisioning services and data science models including operational, data and model health indicators
• Ensure dashboards and alerts are actionable and visible to partners, and that issue diagnosis paths are documented
• Partner closely with Data Science & Decision Science leadership to design, build, and continuously improve a shared internal suite of ML and decisioning tools leveraged across both teams
• Drive alignment on standards, reusable components, and operating patterns that increase velocity while reducing friction from research to production
• Partner with COE on MLOps combined SLOs/SLAs, disaster recovery plans and resource coverage models to ensure continuous reliability of deployed solutions
• Act as the primary interface from Data Science & Decisioning to the ML Eng & Ops COE for platform needs and roadmap input
• Expert SQL and Python skills

Requirements

• Bachelor’s degree in Computer Science, Engineering, Business, or related field required; MBA or advanced degree preferred
• 7+ years in software engineering, platform engineering, data/ML engineering, or MLOps (or equivalent experience)
• 3+ years experience leading teams
• Demonstrated experience deploying and operating production ML systems, especially real-time services
• Strong foundation in modern engineering practices including: CI/CD pipelines, automated testing, release governance, containers and orchestration (Docker/Kubernetes or enterprise equivalent), API/service design (REST/gRPC), performance testing, tuning and observability (metrics/logs/tracing, alerting)
• Experience operating services with on-call, incident response, postmortems, and SLO management
• Proven ability to succeed in a matrixed/org model—driving outcomes through partnership and influence
• Demonstrated ability to navigate complex organizations to achieve priorities
• Working curiosity of the emerging capabilities of artificial intelligence (AI) and how these will impact functional work now and in the future

Tech Stack

• Amazon Redshift
• Cloud
• Docker
• GRPC
• Kubernetes
• Python
• SQL

Benefits

• Flexible work environment
• Fluid career paths
• Internal mobility
• Purpose and well-being focus
• Work-life balance
• Inclusive environment
• Opportunities for volunteering