Job Purpose and responsibilities:
- Proactively drive risk model development end-to-end on complex projects for large-scale financial institutions
- Communicate with Actimize Professional Services and with Clients about model development approach, model performance and recommendations.
- Maintain and analyze information about multiple clients’ models, to derive insights for clients and for enhancing risk model performance.
- Help maintain and improve model development tools.
- Support daily workload management of US analytics delivery team, working with project managers and business analysts.
- 2+ years of experience managing a task force as matrix or direct manager. 5+ years of experience working in integrated development and analytic teams.
- 5+ years of experience working in a global development & / or professional service organization.
- Advanced degree in a quantitative area (statistics, mathematics, physics, computer science, engineering) or the equivalent years in work experience
- Experience with statistical model development. Deep and diverse experience with multiple statistical procedures and data mining algorithms.
- Strong experience with using SQL and EXCEL.
- Strong programming skills in multiple languages and ability to rapidly learn new programming tools. Actual 3+ years of experience with at least 2 of the following languages: R, SAS, Scala, Java, Python, Matlab, SPSS, VBA, including procedures, macros, and scripting.
- Strong general analytical skills, agility with using quantitative tools to solve analytical problems
- Good oral and written communications skills, and ability to interact with engineers, software developers, project managers, business analysts, product managers and with clients.
- Ability to work in multi-disciplinary agile teams.
- Strong commitment to quality.
Additional Desired Qualifications:
- Experience in development of risk management models, particularly in the fraud, AML, or financialtrade compliance areas.
- Knowledge of national and international financial systems and data standards.