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About the Role

Serve as the technical and functional leader for the Data Science Enablement engineering function, owning the production development and ongoing operations of high-profile ML products. You will define the technical strategy, operational maturity, engineering standards, platform capabilities, and long-term effectiveness of the ML software engineering practice, balancing strategic leadership with hands-on technical contribution.

Responsibilities

• Define the technical vision and strategy for ML software engineering initiatives, aligning them with business goals.
• Develop scalable capabilities to power real-time decisioning engines.
• Enable rapid experimentation while ensuring robust, scalable, and secure deployment of ML solutions.
• Establish and evolve engineering standards, operating practices, and technical governance.
• Mentor engineers, provide technical coaching, and promote technical excellence.
• Champion collaboration, continuous improvement, and knowledge sharing.
• Drive alignment across teams through technical influence, architectural guidance, and shared engineering standards.
• Identify capability gaps and drive improvements to tooling, automation, observability, and operational processes.
• Drive consistency in engineering practices and operational processes across teams delivering and supporting ML-powered products.
• Establish operational standards for production ML systems, including reliability objectives, observability, incident management, and support processes.
• Guide the architecture, implementation, deployment, and operation of ML products and reusable components.
• Ensure systems and components meet requirements for scalability, latency, explainability, and regulatory compliance.
• Establish and promote best practices for ML software engineering.
• Stay abreast of industry trends and emerging technologies to drive adoption of modern tools, frameworks, and infrastructure.
• Contribute to QA and code as needed.
• 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 and engineering 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.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related field (PhD a plus).
• 7+ years of ML software engineering, 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 hands-on experience.
• 2+ years of technical leadership experience on an early-stage ML software engineering team.
• 2+ years of data science research 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.
• Experience in a merchant acquiring, payment service provider, or card network environment (bonus).
• Familiarity with tokenization, real-time payments, and the authorization lifecycle (bonus).
• Experience in a large, complex organization in a highly regulated industry (bonus).
• Experience working in an agile environment (bonus).

Skills

• ML software engineering
• ML ops
• ML engineering
• ML research
• Deploying large-scale, real-time ML models
• Microservices development
• API design
• Monitoring
• Deployment strategies
• Containerization
• AWS
• DataBricks
• Data science/ML research process
• Software engineering
• MLOps
• DevOps best practices
• Python
• Pandas
• NumPy
• scikit-learn
• SQL
• NoSQL databases
• Communication
• Leadership
• Stakeholder management
• Tokenization
• Real-time payments
• Authorization lifecycle

Experience Level

• 7+ years of ML software engineering, ML ops, ML engineering, or ML research experience
• 5+ years of experience deploying large-scale, real-time ML models in customer-facing, production environments
• 2+ years of technical leadership experience on an early-stage ML software engineering team
• 2+ years of data science research experience

Education Level

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related field
• PhD a plus

About the Company

• Global Payments and Worldpay recently joined forces.
• Worldpay is committed to protecting the privacy and security of all personal information that we process in order to provide services to our clients.