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Credit Risk Analyst


Job Title:
Credit Risk Analyst

Job Ref:
1774

Location:
Tower Bridge, SE1

Brand:
Home Track

Discipline:
Data & Analytics

Job Description

Hometrack is the leading provider of Automated Valuation Models (AVMs), housing data and analysis to UK lenders, estate agents, government, developers and other key players in the housing market. The company was founded in 1999 and since January 2017 it has become part of ZPG.

Hometrack is seeking to appoint a Risk Modelling Analyst within its Client Analytics team, who has a deep understanding of statistics, mathematics or another quantitative discipline and several years` work experience in credit risk at a UK financial services institution, ideally with a focus on mortgage lending and/or portfolio risk and capital modelling.

Key Responsibilities

  • Conduct detailed analyses and quantitative modelling of property, mortgage and risk data
  • Own key strands of R&D activity and be responsible for model development / enhancement of stand-alone new product solutions or key new features of existing products
  • Develop analytical models from concept to prototype, through close collaboration with the Commercial function for the detailed definition of business requirements and the selection / deployment of the appropriate modelling approach / techniques, including statistical regression, scorecard building, cashflow modelling etc
  • Collaborate with the Engineering function to aid the deployment of complex algorithmic designs and their progression from prototype to the release of production software
  • Provide statistical / mathematical support to the business generally

Skills

  • High-calibre degree in Statistics, Mathematics, Physics or another scientific discipline, an advanced degree is strongly preferred
  • Minimum of 4 years+ hands-on experience in a statistical modelling role preferably in financial services related industry. Applying these skills within the credit risk function of a UK financial services institution, preferably with a focus on mortgage lending and for one or more of the following: scorecard development, portfolio risk modelling (PDs, LGDs etc), regulatory capital, IFRS9 etc
  • Experience in building, developing and validating predictive models (risk or valuation strongly desired)
  • Demonstrable knowledge of building forecast and stress testing models
  • Practical implementation and application of IFRS9 and IAS frameworks
  • Experience of working in a data-rich environment with data mining techniques and predictive analytics, e.g. some of the following: sampling, time-series analysis, clustering, linear and logistic regression, neural networks, genetic algorithms etc
  • Excellent knowledge of probability theory, statistics and calculus
  • Experience with leading statistical software packages and programming languages, R preferred, coupled with excellent knowledge of T-SQL (essential) and ideally SSIS. Experience in data visualisation packages desired.
  • Excellent understanding of mortgage credit risk fundamentals, IRB modelling and regulatory requirements for the UK financial services industry
  • Creative thinking, initiative, and outstanding problem-solving abilities
  • Strenuous attention to detail and strong emphasis on methodological integrity
  • Excellent communication skills and experience with engaging with senior internal and external stakeholders

Salary Range: £50,000 to £70,000

This Vacancy closes for applications on Monday 20th August

About ZPG (www.zpg.co.uk)

ZPG owns and operates some of the UK`s most trusted digital brands that help empower smarter property and household decisions including Zoopla, uSwitch, Money, PrimeLocation and SmartNewHomes.

We are also one of the leading residential property data and software providers with a range of products including Hometrack, Calcasa, TechnicWeb, Ravensworth, Alto, Jupix, ExpertAgent, PropertyFile and MoveIT. Our websites and apps attract over 50 million visits per month and over 25,000 business partners use our services.

ZPG was founded in 2007 and has a highly experienced management team, led by Founder & CEO, Alex Chesterman OBE