Risk - Corporate Risk - Wholesale Credit Solutions - Data Science - Associate

Job Description

OrganizationWholesale Credit Solutions group is a newly created organization spanning across CB, CIB and AWM Credit Risk. This role will sit within the Transformation Office, a centralized team focused on reshaping the end-to-end Wholesale Credit Risk experience, while driving consistency, speed, efficiency across risk management processes. The team is segmented in two focus areas: run the bank (restructuring current processes and policies) and change the bank (implementing strategic processes utilizing new data and machine learning solutions). The Transformation Office team is a motivated, forward thinking team comprised of high-talent individuals with a singular focus of delivering innovative solutions. The position will work extensively with cross-Wholesale risk and technology teams to ensure the implementation of the enhancements to processes, policies and data/analytics.PositionThis role will be part of the Data Science and Strategy Analytics team that has been tasked to develop value added risk analytics solutions through the deployment of advanced analytical frameworks and Machine Learning algorithms on top of the firm s big data resources. In particular, the role will focus on leveraging data to enhance the current End-to-End credit risk process across all of Wholesale.Responsibilities Include but not limited to:

• Analyze structured/unstructured data from internal and external data sources to drive actionable insights in credit risk.
• Lead development of event-driven scenario analytics to analyze the impact of macro-economic factors and current events on the Wholesale portfolio.

• Develop data visualization and summarization techniques to convey key findings in dashboards and presentations to senior management.

• Perform ad-hoc exploratory analysis and data mining tasks on diverse datasets from small scale to big data .
• Execute creative analyses, provide insight to identify issues and implement recommendations
• Gather, synthesize, analyze and present project data and findings. Participate in working groups of senior credit officers.
• Support risk strategy development that improves risk monitoring capabilities through the use of data.
• Support client stakeholders on key business decisions and change efforts. Deliver strategic presentations for both internal and external audiences

• At least 4+ years in a strategy or analytics role with in financial services industry required.
• At least 2+ years of Professional experience as a data scientist or a related analytical role with prior hands-on python coding experience.
• Previous credit risk experience and knowledge of traditional wholesale credit products is a plus
• Master s degree in mathematics, statistics, engineering or equivalent quantitative discipline
• Data Science experience preferred: Regressions, Clustering, Decision Trees, Ensemble learning like Boosting/Bagging, Neural Networks (Network Analysis experience a plus
• Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
• Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.
• Self-starter with the ability to solve problems creatively and deliver results while working in a dynamic, collaborative, and challenging environment
• An ability to work well in cross-functional teams, analyze problems, apply quantitative analytical approaches, and communicate effectively and confidently
• Detail-oriented, organized, and able to multitask to meet deadlines under pressure
• Strong communication skills, both written and verbal, with an ability to communicate business and technical concepts to both expert and novice audiences
• Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, MySQL, etc.

Keyskills :
social network analysisbig datadata miningcredit risktext miningdata sciencerisk analyticsdecision treesrisk managementneural networksrisk monitoringmachine learningnetwork analysis