Job Description

At Early Warning, we’ve powered and protected the U.S. financial system for over thirty years with cutting-edge solutions like Zelle®, Paze℠, and so much more. As a trusted name in payments, we partner with thousands of institutions to increase access to financial services and protect transactions for hundreds of millions of consumers and small businesses.

Positions located in Scottsdale, San Francisco, Chicago, or New York follow a hybrid work model to allow for a more collaborative working environment.

Candidates responding to this posting must independently possess the eligibility to work in the United States, for any employer, at the date of hire. This position is ineligible for employment Visa sponsorship.

At Early Warning, we’ve powered and protected the U.S. financial system for over thirty years with cutting-edge solutions like Zelle®, Paze℠, and so much more. As a trusted name in payments, we partner with thousands of institutions to increase access to financial services and protect transactions for hundreds of millions of consumers and small businesses.

Building and deploying predictive models is at the heart of what we do. Our Machine Learning Operations team enables our Data Scientists to be able to build and deploy innovative models while developing cutting edge, cloud native capabilities to deliver predictive modeling solutions faster, more accurate, and more efficiently to help keep fraud and bad actors out of the banking system.

Overall Purpose

This position supports the platforms, tools, and processes that take our models from ideas to production models, serving predictions in real time. The ML Ops Engineer will partner with our Data Science, Data Product Management, Product Engineering, and Data Platform teams to create and support tools and processes to automate model productionalization.

Essential Functions:

• Designs, builds, and maintains scalable ML infrastructure and pipelines for model training, deployment, and monitoring.
• Optimize orchestration processes to ensure efficient deployment and management of predictive models.
• Monitors and maintains the performance, security, and scalability of the ML infrastructure.
• Collaborates with data scientists and software engineers to streamline the ML lifecycle from development to production.
• Develops and maintains tools for data analysis, experimentation, model versioning, and artifact management. Supports data and model governance requirements as needed.
• Creates robust monitoring systems to measure and trend model performance, detect model drift, and ensure optimal performance of models in production.
• Develops automation scripts and tools to improve the efficiency and reliability of MLOps processes.
• Optimizes ML workflows for efficiency, scalability, and reliability.
• Provide technical assistance to all team members; troubleshoots moderately complex issues and escalates issues as necessary.
• Supports the company commitment to risk management and protecting the integrity and confidentiality of systems and data.
• The above job description is not intended to be an all-inclusive list of duties and standards of the position. Incumbents will follow instructions and perform other related duties as assigned by their supervisor.

Minimum Qualifications

• Education and experience typically obtained through completion of a Bachelor's degree in Computer Science, Engineering, or a related field
• Typically minimum 2 years’ experience or internship in Data Science, ML Engineering or ML Ops capacity.
• Intermediate programming skills in Python and experience with Data Science and ML packages and frameworks.
• Demonstrated experience with AWS services.
• Intermediate proficiency with containerization technologies (Docker, Kubernetes) and CI/CD practices
• Demonstrated experience with MLOps tools such as MLflow, Kubeflow, or similar platforms.
• Understanding and application of data management, distributed computing, and software architecture principles.
• Proven experience delivering real-time models in production environments.
• Background and drug screen.

Preferred Qualifications

• Additional related education and/ or work experience preferred.
• Experience in hybrid (OnPrem / Cloud) environments.
• Hadoop / Hive / Cloudera experience
• Distributed computing programming skills such as Spark
• Experience with Scala / Java programming languages.

Physical Requirements

Early Warning works together in a highly collaborative office environment. Working conditions consist of a normal office environment. Work is primarily sedentary and requires extensive use of a computer and involves sitting for periods of approximately four hours. Work may require occasional standing, walking, kneeling, and reaching. Must be able to lift 10 pounds occasionally and/or negligible amount of force frequently. Requires visual acuity and dexterity to view, prepare, and manipulate documents and office equipment including personal computers. Requires the ability to communicate with internal and/or external customers.

Employee must be able to perform essential functions and physical requirements of position with or without reasonable accommodation.

The base pay scale for this position in:
Phoenix, AZ/ Chicago, IL in USD per year is: $97,000 - $124,000.
San Francisco, CA in USD per year is: $116,000 - $149,000.
Additionally, candidates are eligible for a discretionary incentive plan and benefits.

This pay scale is subject to change and is not necessarily reflective of actual compensation that may be earned, nor a promise of any specific pay for any specific candidate, which is always dependent on legitimate factors considered at the time of job offer. Early Warning Services takes into consideration a variety of factors when determining a competitive salary offer, including, but not limited to, the job scope, market rates and geographic location of a position, candidate’s education, experience, training, and specialized skills or certification(s) in relation to the job requirements and compared with internal equity (peers). The business actively supports and reviews wage equity to ensure that pay decisions are not based on gender, race, national origin, or any other protected classes.

Some of the Ways We Prioritize Your Health and Happiness

• Healthcare Coverage – Competitive medical (PPO/HDHP), dental, and vision plans as well as company contributions to your Health Savings Account (HSA) or pre-tax savings through flexible spending accounts (FSA) for commuting, health & dependent care expenses.

• 401(k) Retirement Plan – Featuring a 100% Company Safe Harbor Match on your first 6% deferral immediately upon eligibility.

• Paid Time Off – Flexible Time Off for Exempt (salaried) employees, as well as generous PTO for Non-Exempt (hourly) employees, plus 11 paid company holidays and a paid volunteer day.

• 12 weeks of Paid Parental Leave

• Maven Family Planning – provides support through your Parenting journey including egg freezing, fertility, adoption, surrogacy, pregnancy, postpartum, early pediatrics, and returning to work.

And SO much more! We continue to enhance our program, so be sure to check our Benefits page here for the latest. Our team can share more during the interview process!

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Early Warning Services, LLC (“Early Warning”) considers for employment, hires, retains and promotes qualified candidates on the basis of ability, potential, and valid qualifications without regard to race, religious creed, religion, color, sex, sexual orientation, genetic information, gender, gender identity, gender expression, age, national origin, ancestry, citizenship, protected veteran or disability status or any factor prohibited by law, and as such affirms in policy and practice to support and promote equal employment opportunity and affirmative action, in accordance with all applicable federal, state, and municipal laws. The company also prohibits discrimination on other bases such as medical condition, marital status or any other factor that is irrelevant to the performance of our employees.