Position at a glance:
This position is within the existing Customer Insights & Data Analytics team in Evalueserve, Chile, supporting a leading US financial services firm.
Responsibilities may include:
• Supporting complex model implementation environments working with large data sets, advanced statistical models, and SAS/other coding to effectively and efficiently execute models for different purposes, including annual stress tests/CCAR, allowance/CECL/IFRS9, and Basel
• Implementation of statistical models with complex logic in SAS or Python production environment
• Monitoring statistical models, including stress testing, allowance, and Basel models, for various asset types, as well as tracking activity and preparing various reports for review and approval from internal stakeholders
• Establishing strong controls and creating consistent and robust execution processes across models
• Maintaining documentation for key implementation processes across the team with focus on standardization of implementation and execution controls
• Extraction, manipulation, and analysis of large data sets from a variety of sources, ensuring quality and consistency
• Creating reports and ad hoc analysis, interpreting and presenting results effectively
• Generating results through automated processes for internal and regulatory exercises
• Pursuing process automation and contributing to the continuous improvement of the programming environment
• Interacting regularly with stakeholders from different teams, such as the modeling, monitoring, governance, and data management teams
País de la oferta: CHILE
Ubicación en el extranjero: Viña del Mar
Requisitos mínimos:
Required Qualifications:
• Bachelor’s/Master’s degree in programs such as mathematics, statistics, engineering, economics or computer sciences
• Excellent verbal and written English communication skills
• 2+ years of hands on experience in SAS programming
• Experience in data cleansing, summarization, and insight generation
• Proficiency in model implementation, execution, monitoring, and/or development of large and complex predictive models for forecasting credit or PPNR losses
• Understanding of predictive modeling techniques and a strong understanding of the statistical testing necessary to assess model performance
• Highly collaborative, flexible, and available during office hours. Willing to take on ownership when asked to work on projects/tasks
• Strong conceptual and quantitative problem-solving skills and ability to think in a structured manner
• Dedicated, enthusiastic, self-driven, and performance-oriented; possessing a strong work ethic
• Reporting and analytical skills, ability to interpret analytical data/results, summarize them, and provide recommendations based on analysis
Preferred Qualifications:
• Hands-on statistical modeling experience
• Experience with developing sound model execution, analytics, or reporting processes
• Experience working with credit risk models, such as stress testing, allowance, or Basel models
• Analytics and/or credit risk management experience in banking
• Experience with Python
• Experience creating code documentation used for auditing and/or the training of other programmers
