Principal Staff Machine Learning Engineer

About your role:

As a Prinicipal/Staff Machine Learning Engineer you will design build and deploy scalable machine learning solutions that support credit risk and fraud management for Global Business Solutions (merchant business). You will collaborate closely with data scientists engineers and business stakeholders to translate complex use cases into resilient ML pipelines and decision frameworks. Your work will directly reduce risk of loss and enable revenue growth for our clients and Fiserv.

What youll do:

• Architect and maintain the machine learning deployment framework used to operationalize models and decisioning engines.
• Partner with stakeholders to define business use cases success criteria and project timelines and own requirements gathering solution design deployment strategy and performance tracking for data science deployments.
• Design build and manage batch and real-time machine learning pipelines to support deployment of models decision frameworks and optimization engines.
• Collaborate with Data and Decision Science team members to build feature libraries feature stores feedback loops and reusable components that streamline model development validation and deployment.
• Own operational governance of the model lifecycle including model versioning promotion rollback deprecation and ongoing monitoring for production machine learning systems.
• Develop and optimize cloud-based data architectures for scalability low latency reliability and cost efficiency implementing robust data quality controls validation checks test coverage monitoring and alerting for production-grade pipelines and data products.
• Deploy data and analytics solutions to Amazon Web Services (AWS) using CI/CD automation and DevOps best practices and maintain clear technical documentation while collaborating within Agile workflows using tools such as JIRA and Confluence.
• Conduct analytics and support development of machine learning and predictive model pipelines and frameworks that advance credit risk and fraud strategies.
• Responsibilities listed are not intended to be all-inclusive and may be modified as necessary.

Experience youll need to have:

• 6 years of experience in machine learning engineering data engineering or data science designing and building production-grade data or ML pipelines in a commercial environment.
• 6 years of experience developing and optimizing data solutions on cloud platforms including hands-on experience with AWS capabilities and Snowflake in an engineering context along with production use of machine learning pipelines feature stores or MLOps practices.
• 6 years of experience creating Python microservices working with containers and developing application programming interfaces (APIs) to support data and machine learning solutions.
• 6 years of experience collaborating with internal and external stakeholders and driving initiatives using clear effective verbal and written communication skills.
• 6 years of experience building scalable low-latency systems or feature components that support analytics machine learning or decisioning use cases.
• 6 years of experience with CI/CD automation and DevOps practices to deploy monitor and support data solutions in production environments.
• Bachelors degree or higher in Mathematics Statistics Computer Science Engineering or a related quantitative field or equivalent combination of education related experience and/or military experience.

Experience that would be great to have:

• Experience in machine learning operations.
• Experience working in Agile delivery environments using tools such as JIRA and Confluence or similar work-tracking and collaboration platforms.
• Experience in financial technology payments merchant acquiring or lending domains especially in credit risk or fraud management.
• Experience with infrastructure-as-code tooling (such as Terraform or CloudFormation) and container orchestration platforms (such as Kubernetes or Amazon Elastic Kubernetes Service).

How youll work:

• This role is on-site Monday through Friday. Fiserv considers in-person collaboration to be an essential part of this role as in-person office experiences help you with your overall onboarding experience and leads to stronger productivity.
• This role requires use of a computer and audio equipment.

Benefits at Fiserv:

• Fuel Your Life program to support your physical financial social and emotional well-being.
• Paid holidays and generous time away policies.
• No-cost mental health support through Employee Assistance Programs.
• Living Proof program to recognize your peers extra effort with points redeemable for rewards.
• Eight Employee Resource Groups to foster a collaborative culture and expand your network.
• Unparalleled professional growth with training development and internal mobility opportunities.
• Medical dental vision life and disability insurance options available from day one.
• Retirement planning and discounted shares with the Employee Stock Purchase Plan.
• Tuition assistance and reimbursement program.
• Paid parental caregiver and military leave.