Are you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area? This is a unique opportunity for you to work in our team to partner with the Business to provide a comprehensive view.

As Quantitative Research Data Engineering Vice President you will be a senior member of the Wholesale Credit Data QR team. Wherein the team's mission is to design, analyze, deliver firm-wide Data to support the firm’s Wholesale Credit Stress (CCAR, ICAAP, Risk Appetite) and Loan loss reserves models. In particular, for this role, the team focuses on Data model definition, evolution of Data Dictionary to enable deep dive Data Analysis and Analytical explorations. The successful candidate will work on the evolution of our frameworks, underlying Data platforms and related tools to enhance ease of integration of pricing and forecast models, improve flexibility and extendibility of the framework as well as improve scalability and performance. This will require the candidates to work with other more experienced Wholesale Credit model developers and business partners and provide guidance to junior team members.

Job Responsibilities

You will work as data engineer, to create or build data pipeline, define API to source data from different systems, perform complex transformation or enhancements to data and optimize end to end run. Perform data analysis to support model development and analytics Liaise with various lines of business and risk modelers, thoroughly understand various models for BASEL, CCAR, CECL and other credit risk models Work with multiple stakeholders to elicit, analyze, refine and document business process and data requirements Collaborate through the entire Software Development Life Cycle (SDLC) including planning, analysis and testing of new applications and enhancements to existing applications Perform user acceptance testing and deliver demos to stakeholders by SQL queries or Python scripts

Required qualifications, capabilities, and skills

Bachelor’s or Master’s in Computer Science, Data Analytics or equivalent discipline. Experience of 7+ years in data engineering role in financial services, data analytics with focus on frameworks to handle large datasets and similar. Data Analysis and data manipulation skills using SQL , Python, object orient programming & MS Excel is required. Strong analytical skills in forecasting and interpreting results and comfortable working with large quantities of data Prior experience on building data architecture to source data from different systems, handling complex transformation ad optimizing the end to end solution Ability to solve problems creatively while working in a dynamic and challenging environment under tight deadlines. Eagerness to learn about Credit Risk, Risk Parameters, Regulatory and Accounting concepts Detail oriented and strong organizational skills. Excellent communication abilities, both written and oral Experience implementing analytics frameworks in finance. Experience with source control, automated build/test systems, code coverage, unit testing and release processes

Preferred qualifications, capabilities, and skills

Experience in software engineering to build data architecture based on python, object orient programming, SQL etc. is preferable Knowledge of Wholesale Credit, CCAR, Allowance (IFRS 9/CECL), Basel II/III regulatory capital Proven ability to develop collaborative relationships with key internal partners to achieve objectives and prioritizations