Data Scientist II - Contract

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.

Data Scientist II – Model Validation and Monitoring

Overall Purpose

This position serves as a data science team member in the Model Validation and Monitoring Team delivering leading edge machine learning models to our clients.  This includes providing effective challenges to model development, conduct model monitoring and performance tracking, provide root cause analysis of model performance, exploring, building, validating, and deploying models.

Essential Functions

• Lead model monitoring activities, including tracking performance metrics, detecting model and data drift, identifying data quality issues, providing root cause analysis, and recommending remediation strategies.

• Conduct rigorous model validation by providing effective challenges during model development phases, including performance testing, benchmarking, provide remediation plan, and documentation to ensure models meet business, technical, and regulatory standards.

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

• 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. 

Minimum Qualifications

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

• A minimum of 2 years of 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 with Spark language.

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

• 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. 

• Experience of using advanced ML algorithms building, testing, and deploying fraud models.

• Hands-on experience with PySpark

• 2+ years of industry experience in building or validating machine learning models

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

• Experience exploring data and finding hidden patterns and data anomalies

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 occasi