Senior Manager of ML Ops

Worldpay•Cincinnati, OH

5d•Hybrid

About The Position

Are you ready to write your next chapter? Make your mark at one of the biggest names in payments. With proven technology, we process the largest volume of payments in the world, driving the global economy every day. When you join Worldpay, you join a global community of experts and changemakers, working to reinvent an industry by constantly evolving how we work and making the way millions of people pay easier, every day. What makes a Worldpayer? It’s simple: Think, Act, Win. We stay curious, always asking the right questions to be better every day, finding creative solutions to simplify the complex. We’re dynamic, every Worldpayer is empowered to make the right decisions for their customers. And we’re determined, always staying open – winning and failing as one. We’re looking for a Senior Manager of ML Ops to join our ever evolving Data Science & AI team to help us unleash the potential of every business. Are you ready to make your mark? Then you sound like a Worldpayer. About the team Our Product and Technology teams are the Worldpayers behind the game-changing products and digital experiences we’re best known for. Striving for better, they never stand still — delivering impactful innovations that power transactions across the world. Worldpay’s Data Science & AI team deploys innovative data-driven products for Worldpay’s customers, generating billions of dollars of measurable value for merchants every year – and we’re just getting started. The work we do is highly visible and at the center of Worldpay’s core strategy. What you’ll own We are seeking an experienced and visionary Head of Machine Learning Ops to lead and scale our ML Operations team. In this strategic role, you will collaborate closely with research-focused data scientists and other product delivery teams to translate ML models and data-driven algorithms into robust, scalable, and production-ready products that optimize the payment process. You will inherit a relatively small but high-impact team with a mandate to grow and mature the function to meet expanding business and technical needs. This role will require a combination of people, technical, and organizational leadership. While this is primarily a leadership role, you will need to roll up your sleeves to contribute technically as needed, especially in the early days.

Requirements

Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related field (PhD a plus).
7+ years of ML ops, ML engineering, or ML research experience.
5+ years of experience deploying large-scale, real-time ML models in customer-facing, production environments, including significant experience hands on.
3+ years of people leadership experience.
Proven experience developing microservices at scale (API design, monitoring, deployment strategies, containerization) in a cloud environment (preferably AWS and DataBricks).
Strong understanding of the data science/ML research process.
Strong understanding of software engineering, MLOps, and DevOps best practices.
Strong Python skills, including in relevant libraries such as Pandas, NumPy, scikit-learn.
Proficiency in SQL and NoSQL databases.
Excellent communication, leadership, and stakeholder management skills.

Nice To Haves

Experience in a merchant acquiring, payment service provider, or card network environment.
Familiarity with tokenization, real-time payments, and the authorization lifecycle.
Experience in a large, complex organization in a highly regulated industry.
Experience working in an agile environment.

Responsibilities

Strategy and vision: Define the technical vision and strategy for ML operations initiatives, aligning them with business goals.
Develop scalable capabilities to power real-time decisioning engines throughout the payment lifecycle and beyond.
Enable rapid experimentation while ensuring robust, scalable, and secure deployment of ML solutions.
Team leadership: Recruit, hire, mentor, coach, and retain an elite team of ML Ops Engineers and ML Engineers.
Foster a culture of innovation, experimentation, collaboration, and continuous learning.
Ensure projects are delivered on time and to a high quality standard.
Technical leadership and contribution: Oversee the development, validation, production deployment, operation, and maintenance of ML models and systems.
Contribute to QA and code as needed, especially in the early days.
Ensure systems and components meet requirements for scalability, latency, explainability, and regulatory compliance.
Establish and ensure adherence to best practices for ML engineering and MLOps.
Stay abreast of industry trends and emerging technologies in ML, driving adoption of modern tools, frameworks, and infrastructure.
Work with data governance and legal teams to ensure compliance with data privacy regulations (e.g., PCI-DSS, GDPR).
Cross-functional collaboration: Partner closely with research-focused data science teams, business stakeholders, infrastructure support teams, data engineering teams, security/compliance teams, etc. to identify opportunities and incorporate ML into products and systems.
Collaborate with other Data Science leaders to establish an operating model for machine learning R&D that optimizes end-to-end delivery of business value.
Communicate complex technical concepts to non-technical stakeholders effectively.