Data Scientist II - Model Validation and Monitoring

Data Scientist II – Model Validation and Monitoring 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 include: 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 include: 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 include: Experience of using advanced ML algorithms building, testing, and deploying fraud models. United IT