Job Details

Job Description Job Description Role : Data Scientist II Model Validation and MonitoringLocation: 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, PurposeThis 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 FunctionsLead 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 performanceWrite production level code in a dynamic, start-up environmentSolve complex problems using terabyte size data setsApply of a variety of machine learning techniques to a business problem to arrive at optimal approachPartner with Product and Engineering teams to solve problems and identify trends and opportunitiesExplain and visualize results and algorithm performance to non-technical audiencesMinimum QualificationsA 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 performanceExperience using ML libraries, such as scikit-learn, mllib, etc.Experience using data visualization toolsAble to write production level code, which is well-written and explainableAbility to effectively communicate findings from complex analyses to non-technical audiences.Preferred QualificationsExperience of using advanced ML algorithms building, testing, and deploying fraud models.Hands-on experience with PySparkIndustry experience in building or validating machine learning modelsExperience exploring data and finding hidden patterns and data anomalies