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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.
ESSENTIAL FUNCTIONS:
- 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. While the COE owns the platform, this team owns the use-case pipelines, performance, and operational health that depend on it.
- 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.
QUALIFICATIONS:
- 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 / hub-and-spoke 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.
We are an equal opportunity employer with a commitment to diversity. All individuals, regardless of personal characteristics, are encouraged to apply. All qualified applicants will receive consideration for employment without regard to age, race, color, national origin, ancestry, sex, sexual orientation, gender, gender identity, gender expression, marital status, pregnancy, religion, physical or mental disability, military or veteran status, genetic information, or any other status protected by applicable state or local law.
Top Skills
What We Do
Built on a foundation of trust, integrity and promise, we proudly serve over 71,000 outstanding organizations and more than 17 million individuals. ¹ We take great pride in helping people with saving, investing and advice, while providing them with the tools and resources they need to help reach their financial goals. We’re continuing to grow — and innovate — every day. Our mission is to empower financial freedom for all. That mission starts by delivering advice, personalized guidance and critical support. We strive to meet the unique needs of everyone we serve and embrace the opportunity to inspire them along their journey. Disclosures: https://www.empower.com/social-media
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