Illinois Staffing
Data Analyst, Funding Risk
Analyze and measure exposure to credit and market risk threatening the assets, earning capacity, or economic state of an organization. Make recommendations to limit risk. Analyze areas of potential risk to the assets, earning capacity, or success of organizations. Conduct statistical analyses to quantify risk, using statistical analysis software or econometric models. Work in the fraud risk team and will play a key role in evaluating, monitoring portfolio and mitigating losses (fraud and synthetic ID losses). Work closely with the team on evaluating and monitoring key risk metrics related to fraud losses and member experience for example dispute rates, credits, reversals, chargebacks, recovery rates, write-offs, etc. Deep dive into past and ongoing trends and provide data driven insights to create solutions to problems as well as dashboards for constant monitoring. Create strategies to mitigate losses and partner with xfn teams (product, engg, ops) to implement them. Create dashboards and monitor performance across relevant dimensions and create alerts to highlight any anomalies or unusual trends. Work closely with Data Science and Machine Learning teams to segment customers based on risk and help automate the processes to build scalable solutions. Work with large datasets. Leverage SQL programming skills, advanced Microsoft Excel skills, and preferably experience in Python, R, SAS, or similar language. Create portfolio performance monitoring dashboards using Tableau, Looker, Hex, or a similar business intelligence tool. Utilize knowledge of the fundamentals of banking customer onboarding/KYC, payment processing, and an understanding of industry risk trends, including familiarity with risk strategy development. Some telecommuting is permitted. Minimum Requirements: Masters degree in Mathematics, Statistics, Business Analytics or related field and 2 years of experience in the job offered or in a data science/analyst-related occupation. Some telecommuting is permitted. Special Requirements: Position requires at least 2 years of experience in each of the following skills: Utilize experience with analytical skills for deep diving into historical and ongoing trends to provide data-driven insights and identify solutions to complex problems; Utilize knowledge of tools like Tableau, Looker, Hex or similar business intelligence platforms to build and maintain performance monitoring dashboards; Utilize knowledge of advanced SQL for working with large datasets and extracting meaningful insights; Utilize knowledge of Python, R, SAS or a similar programming language for data analysis and scalable solution development; Utilize knowledge of segmenting segment customers based on risk profiles and automate scalable processes; Utilize knowledge of banking fundamentals including customer onboarding/KYC processes, payment processing, and industry risk trends. The base offered for this role and level of experience is reflected above. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.
Analyze and measure exposure to credit and market risk threatening the assets, earning capacity, or economic state of an organization. Make recommendations to limit risk. Analyze areas of potential risk to the assets, earning capacity, or success of organizations. Conduct statistical analyses to quantify risk, using statistical analysis software or econometric models. Work in the fraud risk team and will play a key role in evaluating, monitoring portfolio and mitigating losses (fraud and synthetic ID losses). Work closely with the team on evaluating and monitoring key risk metrics related to fraud losses and member experience for example dispute rates, credits, reversals, chargebacks, recovery rates, write-offs, etc. Deep dive into past and ongoing trends and provide data driven insights to create solutions to problems as well as dashboards for constant monitoring. Create strategies to mitigate losses and partner with xfn teams (product, engg, ops) to implement them. Create dashboards and monitor performance across relevant dimensions and create alerts to highlight any anomalies or unusual trends. Work closely with Data Science and Machine Learning teams to segment customers based on risk and help automate the processes to build scalable solutions. Work with large datasets. Leverage SQL programming skills, advanced Microsoft Excel skills, and preferably experience in Python, R, SAS, or similar language. Create portfolio performance monitoring dashboards using Tableau, Looker, Hex, or a similar business intelligence tool. Utilize knowledge of the fundamentals of banking customer onboarding/KYC, payment processing, and an understanding of industry risk trends, including familiarity with risk strategy development. Some telecommuting is permitted. Minimum Requirements: Masters degree in Mathematics, Statistics, Business Analytics or related field and 2 years of experience in the job offered or in a data science/analyst-related occupation. Some telecommuting is permitted. Special Requirements: Position requires at least 2 years of experience in each of the following skills: Utilize experience with analytical skills for deep diving into historical and ongoing trends to provide data-driven insights and identify solutions to complex problems; Utilize knowledge of tools like Tableau, Looker, Hex or similar business intelligence platforms to build and maintain performance monitoring dashboards; Utilize knowledge of advanced SQL for working with large datasets and extracting meaningful insights; Utilize knowledge of Python, R, SAS or a similar programming language for data analysis and scalable solution development; Utilize knowledge of segmenting segment customers based on risk profiles and automate scalable processes; Utilize knowledge of banking fundamentals including customer onboarding/KYC processes, payment processing, and industry risk trends. The base offered for this role and level of experience is reflected above. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.