Paze Product Data Scientist II

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.

Overall Purpose

This role applies data science, statistical analysis, experimentation, and machine learning techniques to support consumer product strategy, product performance, customer experience, and growth opportunities. While the role includes model building and advanced quantitative methods, the primary focus is using data science to inform and improve the Paze product experience.

Essential Functions

• Explore and aggregate data independently to uncover data anomalies that impact algorithm performance

• End to end feature engineering - brainstorm, create, validate, down-select, etc.

• Write production level code in a dynamic, start-up environment

• Solve complex problems using terabyte size data sets

• Apply of a variety of machine learning techniques to a business problem to arrive at optimal approach

• Partner with Product and Engineering teams to solve problems and identify trends and opportunities

• Explain and visualize results and algorithm performance to non-technical audiences

• Support the company's commitment to protect the integrity and confidentiality of systems and data.

• Support the company's commitment to protect the integrity and confidentiality of systems and data.

Paze specific:

• Apply data science and statistical methods to evaluate Paze product performance, customer behavior, feature adoption, conversion, retention, and product friction.

• Partner with Product Management, Engineering, Fraud, Marketing, Design, Operations, and Data Engineering to define success metrics, evaluate hypotheses, and translate insights into product decisions.

• Develop analytical frameworks, experimentation approaches, segmentation, forecasting, or predictive models to support Paze product strategy and roadmap priorities.

• Translate complex data findings, statistical results, and model outputs into clear, actionable recommendations for product teams, business partners, and senior leadership.

• Perform data profiling and validation to ensure analysis is based on accurate, well-understood data; identify data quality issues, risks, and trends and recommend improvements.

Minimum Qualifications

• Bachelor’s Degree in Mathematics, Statistics, Computer Science, Operational Research or related field;

• Typically a minimum of 4 years data science, engineering, mathematics, or related work experience is required.

• Experience developing data science pipelines & workflows in Python, R or equivalent programming language. Experience in writing and tuning SQL. Experience handling terabyte size datasets

• Experience applying various machine learning techniques, and understanding the key parameters that affect their performance

• Experience using ML libraries, such as scikit-learn, mllib, etc.

• Experience using data visualization tools

• Able to write production level code, which is well-written and explainable

• Interest to do lots and lots of proof of concepts/rapid prototyping

• Ability to effectively communicate findings from complex analyses to non-technical audiences.

• Background and drug screen

Preferred Qualifications

• PhD/MSc in Mathematics, Statistics, Computer Science, Operational Research or related field; Advanced degree preferred. 

• Knowledge of advanced ML algorithms

• 2+ years of industry experience in machine learning

• Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment

• Experience exploring data and finding hidden patterns

• Prior experience in payments, financial services, fintech, consumer product, marketplace, consulting, or another data-rich product environment.

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