Head of Decision Science

London
£80000 - £110000 per annum + Bonus + Competitive Benefits

Head of Decision Science
London
Up to £110,000

This is a fantastic opportunity for someone with experience developing Application and Behavioural Scorecards to join a profitable, growing fintech business and help solve engaging business problems across credit risk, lending, pricing, marketing etc.

The business is growing internationally with new product launches planned for US and Europe. They are also completely revamping their models and strategies across the lending team, so it's the chance to come in and build best in class models and solutions from scratch.

As a senior leader in a flat structured business you will use your hands on technical skills to develop best in class decision models, whilst also having ownership over strategic decisions across Credit Risk decisioning, strategy, propensity modelling etc.. You'll also have significant influence on C-level and board decisions.

THE ROLE:

  • Manage the development, monitoring and deployment of Application and Behavioural Credit Risk Scorecards for unsecured lending products
  • Use your analytical strengths and data skills to solve different business problems ranging from Credit Risk to Marketing, Pricing etc
  • Use of techincal tools such as Python, SQl, SAS, R to develop and optimise decision models
  • Manage the analysis and optimisation of existing Credit Risk Scorecard models
  • Involvement in leadership decisions across the Credit Risk and Analytics function
  • Influence stakeholders up to board level and represent Decision Science and Modelling functions at various committee meetings
  • Eventually building out and training the Decision Science team

YOUR SKILLS AND EXPERIENCE

  • Educated to a degree level in a numerate discipline
  • Strong Credit Risk Scorecard development (Credit Scoring) experience with Python or similar statistical modelling tools
  • Experience working with Credit Reference Agency data e.g. Equifax, Experian, TransUnion etc
  • Track record of finding and implementing efficient solutions to real business problems in the lending space
  • Self driven able to motivate team and manage both internal and external relationships

THE BENEFITS

  • Up to £110,000 + Bonus
  • Competitive Benefits Package
  • Opportunity to step up into a Head Of level role
  • Provide direction to a growing global business

HOW TO APPLY:

Use the 'apply' feature on this page

KEYWORDS:

Credit Risk , Credit Underwriting Models, Scorecards, Decision Science, Application Scorecards, Behavioural Scorecards, Stress Testing, Python, SAS, R, Credit Risk Modelling, Acquisition Strategies, Cards, Loans, Unsecured

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95070/cl.
London
£80000 - £110000 per annum + Bonus + Competitive Benefits
  1. Permanent
  2. Decision Science

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Harnham blog & news

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Visit our Blogs & News portal or check out our recent posts below.

Mitigating Risk In The Financial Services Sector With Machine Learning

Data & Analytics is an industry that is constantly evolving and is always using the latest technology to innovate its services and capabilities. More recently, these advancements have moved in areas such as Artificial Intelligence (AI) and Machine Learning (ML). Machine Learning is a method of data analysis, under the branch of Artificial Intelligence, that allows systems to learn from data, identify patterns and ultimately make decisions with little to no human intervention. Used across a vast range of sectors, this arm of Data & Analytics has become widely popular, especially within highly-advanced industries such as Finance.  Since the 2008 financial crash, at the top of the agenda for many Financial Institutions (FIs) was, and still is, the need to protect business, increase profitability and, possibly most importantly, address the abundant inadequacies of risk management. This includes risks posed by consumers such as liquidity, insolvency, model and sovereign, as well as any internal process and operational risk the FIs may also be facing through any failures or glitches.   Machine Learning has played a crucial role in improving the quality and precision of FIs risk management abilities. In HPC Wire, it has been reported that the use of AI and ML within the financial sphere to mitigate risk, improve insights and develop new offerings may generate more than $250 billion for the banking industry.  How does ML work? By using incredibly large data sets, drawn (with consent) from consumers, ML can learn, and predict, patterns in consumer behaviour. This can be done in one of two ways: through supervised learning tools, or unsupervised learning tools.  Explained by Aziz and Dowling; “In supervised learning you have input data that you wish to test to determine an output. 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In this space, the evidence for the effectiveness of ML is overwhelming. Khandani et al. found that FIs using ML to analyse and review credit risk can lead to a 25 per cent cost saving for the FI involved.   These ML models come in various shapes and sizes, with the most common being instantaneous apps or websites which allow users and their banks to have access to real-time scoring, data visualisation tools and business intelligence tools.  The risk of risk management with ML Like with any AI or ML application or tool, there will always be cause for concern and real need to always remain vigilant. While ML has shown to be an invaluable tool across lower risk areas, the complexity of more statistical areas of banking, such as loans, has proven to be an Achilles heel for the technology. This usually stems from bias, a perpetual problem for AI and ML across all industries.  Technology Review notes that “There are two main ways that bias shows up in training data: either the data you collect is unrepresentative of reality, or it reflects existing prejudices.”  Data, analytics, AI and ML are notoriously non-diverse working sectors. The person behind the screen creating learning algorithms tends to be white and male, and very unrepresentative of the whole society that the machine learning tool will serve. Over the years, we have heard numerous accounts of unfair and unjust machines, which have learned from a very narrow and unrepresentative dataset, which stems from the lack of diversity amongst the employees within the Data & Analytics industry. For example, Microsoft’s racist bot and Amazon’s sexist recruitment tool, both clear examples that ML and AI are not ready to be used on their own, and humans still need to play an integral part in decision making.  Banks and FIs must be aware of the, potentially lethal, consequences that bias in ML may present. Lenders must be careful to ensure they are working within the guidelines of fair lending laws and that no one group of people are being penalised for no reason other than issues within the technology and its algorithms. It is vital that the humans behind the technology don’t rely on ML to provide them with an answer 100 per cent of the time but, instead, use it to aid them in their decision making when it comes to risk mitigation.  If you’re looking for a role in Data & Analytics or are interested in finance or Risk Analytics, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to learn more.  

Trade Analysts Keep Money Flowing on the Field

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