Data Scientist

KAYGEN is an emerging leader in providing top talent for technology based staffing services. We specialize in providing high-volume contingent staffing, direct hire staffing and project based solutions to companies worldwide ranging from startups to Fortune 500 and Managed Service Providers (MSP) across a wide variety of industriesDescription:We are looking for a Data Scientist to join our global AI product support team. This scientist will report to our Senior Director of Global Data Science Products. They will play a critical role in the rollout of our global suite of AI products across consumer lending, business intelligence services and health product suite.This role will cover a wide range of responsibilities with plenty of variation from week to week.Responsibilities will include but are not limited to: Product developmento Prototype new modeling methodologies for incorporation into our global suiteo Work directly with our Client engineering team to identify issues and guide them on requirementso Write data pipelines / ETL for non-standard or new modeling use caseso Build new dashboards and reports to provide insights to clients on model performance and impact, leveraging our proprietary BI toolkito Help document methodologies, tool usage and business logic Client & user supporto Support client implementation teams executing complex modeling tasks on our enterprise Client ops platformo Perform complex/custom data engineering where needed to support strategic clientso Train Client models for underwriting, targeting and other applications where needed Testing & QAo Test new product features and algorithms and verify accurate implementation ahead of live release to our global client baseo Implement bug-fixes and upgrades to Our Client methodologies in credit-related business logic (e.g. Bureau Inferencing modifications etc.)Valuable skills include Highly proficient writing data processing code with at least one SQL dialecto Spark SQL experience desirable but not required Comfortable using Python and Jupyter notebooks with common open source libraries for machine learningo MLlib, pytorch, sklearn, tensorflow all valuable Understanding of credit risk modeling fundamentals Strong communication skills - able to talk to business users to understand and articulate issues requirements, as well as more technical developer audiences Strong data visualization skills - can present datasets to bring out insights & tell a story Self-starter willing to solve problems and ask the right questions; growth mindset Desirable: Familiarity with widely-used credit risk assessment tools like FICO Model Builder and SAS Desirable: Understanding of the credit risk lifecycle from prospecting and acquisition to customer management and collectionsWhat your background looks like Experience in modeling and/or data engineering in a Credit Risk institution. You will have worked at least 3 - 5 years for a major bank, FinTech or other financial institution. At least 5 - 7 years total industry experience focused on analytics, data science and/or data engineering, with some direct experience in applied statistics and machine learning Advanced degrees in quantitative fields and/or professional experience in applied analytics are valuable Bachelor s degree required, Master s degree preferred