Role : Data Scientist II – Model Validation and Monitoring

Location: Scottsdale AZ (Onsite)

*US Citizen & GC Only

**Must be legally authorized to work in US without need for employer sponsorship now or at any time in the future.

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

Minimum Qualifications

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.

Preferred Qualifications

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

Hands-on experience with PySpark

Industry experience in building or validating machine learning models

Experience exploring data and finding hidden patterns and data anomalies