Statistical Modeller - Treasury Behavioural Modelling Python (AVP/VP)

  • Exec+bens
  • London, England, United Kingdom
  • Permanent, Full time
  • Charles Levick
  • 30 Sep 16

My client, a tier 1 bank, is recruiting an experienced Quantitative Analyst & Modeller to join their team. The candidate must have experience in quantitative finance and using Python to build statistical models. Statistical Modelling experience and good Python or R knowledge required. Strong C++ candidates will be considered if strong statistical modellers.

~~The Quantitative Analytics group (QA) is a part of the IB Markets division. QA supports critical functions within the Group such as Group Treasury, Credit, Market and Counterparty Risk and Finance. The group also supports trading businesses within Markets such as Macro (Rates, FX, and Commodities), Equity, Credit and CCM (Counterparty Risk Trading and Loan Portfolio Management). Additionally, QA partners with Trading, Risk, Treasury and Finance to deliver solutions for:
•         Risk management
•         Capital utilization
•         Funding planning and liquidity management
•         Regulatory requirements
•         Business innovation
•         Changes in markets structure
The Asset Liability Management group within QA is of central importance in managing the bank’s funding requirements and liquidity risk effectively and within the new regulatory environment.
The primary role of the team is to develop and deploy industry leading behavioural models for measuring and managing funding and duration risk. The behavioural models apply primarily to banking book asset and liability products such as loans, revolvers, term deposits etc and are used for forecasting future expected portfolio balances taking behavioural characteristics into account. These models are key components of lifetime expected loss calculation for IFRS9, Comprehensive Capital Analysis and Review (CCAR), Funds Transfer Pricing system and support Group Treasury function in determining optimal funding strategies.
Overall purpose
This is a quantitative analyst role within QA Asset & Liability management with responsibility for researching, developing and maintaining behavioural models and IT infrastructure related to IFRS9 regulatory requirements and Treasury functions, to help support quantification and pricing of liquidity and funding risk associated with the bank’s asset / liability profile.
Key Accountabilities
•         Design, build and deliver robust and production quality behavioural models and code within a unified library for use within Treasury and Corporate and International.
•         Assist with the systematic review and on-going assessment of existing models for forecasting asset and liability behavioural balances.
•         Liaise with Treasury and business stakeholders to ensure that the model meets their requirements and ensure that they agree with the modelling assumptions and understand the associated risks.
•         Deliver high quality documentation and presentations to support and maintain model and library use.
Stakeholder Management and Leadership
•         Facilitate and challenge discussion of modelling options with senior model owners.
•         Assist the gathering and documentation of model requirements.
•         Communication of model development progress, including attendance at regular working groups and escalation of QA modelling dependencies, risk and issues to senior stakeholders.
•         Handover to Treasury to manage system implementation and integration of QA functionality into Treasury architecture
•         Communication and explanation of model results to stakeholders.
Decision-making and Problem Solving
•         Ability to convert business objectives into technical modelling solutions.
•         Data analysis for trends and signal extraction.
Risk and Control Objective
•         Ensure that all activities and duties are carried out in full compliance with regulatory requirements, Enterprise Wide Risk Management Framework and internal Policies and Policy Standards
Person Specification Candidate

Qualifications & Education
•         Post graduate degree in a quantitative/Statistical discipline.
•         PhD in a quantitative discipline.

Candidate Experience
•         Strong industry experience in quantitative finance. This may be replaced by relevant academic or industrial experience in statistics.
•         Able to deliver to tight deadlines on quantitative projects.
•         Python (preferred) or C++ development experience.
•         Experience in designing and developing statistical and econometric models.
•         Experience in analysing large volumes of data including cleaning and subsequent pattern identification and clustering.
•         Knowledge of EAD modelling.

Candidate Skills and Knowledge
•         Good understanding of statistical and econometric modelling techniques – e.g. time series analysis, regression models and various estimation techniques.