Paze Product Data Scientist II

Data Scientist

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.

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.
• Experience partnering directly with Product Managers, product leadership, or cross-functional product teams.
• Experience with experimentation, A/B testing, causal inference, segmentation, funnel analysis, cohort analysis, forecasting, or predictive modeling.

Physical Requirements

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 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.Early Warning Services is an equal opportunity employer.

The base pay scale for this position in: New York, NY/ San Francisco, CA in USD per year is: $122,000 - $165,000. 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.