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Senior Manager – Tech Risk Analytics – Wholesale Banking

Date: Aug 9, 2019

Location: London, United Kingdom

Company: KPMG UK

The Team

Using a wide variety of technical and sector-specific skills, KPMG's Risk Consulting group proactively helps clients increase profits whilst reducing reputational, operational, financial and other risks. We are experienced in managing diverse issues including fraud, regulatory compliance, risk frameworks and modelling, capital efficiency, corporate governance, dispute resolution, deriving value from contracts and much more.
Technology - Our clients need to deal effectively with technology related risks and derive maximum value from data and documentation. Our specialists provide independent, jargon free advice and advanced technology capabilities to help our clients proactively manage their technology risks and use their data to its full potential.
Job Description: Joining KPMG means joining a talented team of exceptional colleagues who bring innovative thoughts and a natural curiosity to the work they do each day. No one type of person succeeds at KPMG; a diverse business requires diverse personalities, characters and perspectives. There really is a place for you here.

The Role

• Build global relationships and actively seek out the global networks best experts to address client needs
• Communicate compelling and well thought out solutions to complex problems
• Build constructive working relationships across different teams, functions, countries or cultures
• Exhibit deep understanding of wholesale banking and capital markets (buy and/or sell side) - key performance drivers, emerging technical and industry developments, general business/economic/regulatory developments and their impact to the client
• Leverage statistical and advanced analytical techniques to provide new insights into business performance issues for wholesale banking and capital markets clients and recommend changes to improve profitability, risk management and regulatory compliance
• Provide oversight on the delivery of data analytics projects leading large teams, coach junior staff and regularly conduct trainings/knowledge sharing sessions on industry challenges and how analytics can help address those challenges
• Identify and capitalise on opportunities to expand business by building and executing on a pipeline of proactive propositions for wholesale banking and capital markets clients. Assume ownership of key client relationships, identify revenue-generating opportunities and get involved in business development activities such as proposition development, proposal writing, pitch presentations, etc.
• Ensure the firm’s quality protocols / risk management requirements are implemented and complied with both internally and on client engagements
• Build relationships with other departments to deliver market and client focused business solutions

The Person

• Recognises the importance of continuous self and team development and actively strives to achieve this.
• Helps others to understand how their work contributes to the overall success of an engagement and the wider firm
• Fosters a sense of self belief and confidence in others
• Seeks to understand others motivations
• Supports others to make brave decisions
• Cultivates a culture of resourcefulness and creative thinking

Essential Skills:
• Demonstrable expertise in providing analytical solutions to wholesale and capital markets sector to improve business/risk/regulatory performance. Experience of working with a tier 1 consulting firm or a large Financial Services Institution as a Data Scientist is highly desirable
• Strong understanding of various asset classes (such as Equities, Derivatives, Fixed Income linear and non-linear derivatives, FX, Rates, Repo / SBL, Macro- and Structured-Products, Securitizations and Credit) on the entire trade life cycle
• Extensive experience of deploying advanced analytical techniques (such as statistical modelling, machine learning, NLP, optimisation, simulation, forecasting, information retrieval etc.) to generate insights
• Familiarity with pricing and risk analytics topics such as derivative valuation, VaR, ES, Stress Testing, Scenario Analysis, Calculations, Risk Sensitivities , Monte Carlo, Historical Simulations, Time Series, Derivatives Pricing, CAD2, Internal Model Method (IMM), Risk not in VaR (RNIV)
• Deep understanding of middle & back office processes, preferably engaged in operational strategy across investment operations to include Reporting (client, regulatory and fund performance), Trade Processing, Valuations, Distribution Operations, Mandate Control, client on boarding and transitions
• Deep technical grounding on data challenges in a wide range of wholesale banking and capital markets risk and regulatory issues such as FRTB, Basel III, MiFID II, EMIR-REFIT, ESMA, CSDR, SFTR, Benchmarks, Libor reform, CCP issues, CRAs etc.
• Exposure Risk Modelling - Good understanding of credit risk exposure models used for risk management, setting capital requirements, stress testing and expected loss calculations
• Experience across a range of analytical tools (Hadoop, Beeline, Hive, SAS, SPSS, Matlab, Pentaho, R, Python, Qlikview, Spotfire, Tableau, Excel (VBA, Pivot Tables), etc.)
• Strong leadership skills both on engagements and in an office environment
• A proven ability to lead complex engagements, manage and monitor budgets and financial performance
• Proven sales experience and ability to play an active role in the business development process
• Appreciation of growth areas in wholesale banking and capital markets and how analytics can be leveraged to create values for our clients
• Ability to spot opportunities to add value to clients and work with colleagues in other lines of service to help clients find solutions to the challenges they face

Desirable Skills:
• Experience of working in Front Office quant role
• Interest Rate benchmark reforming experience (Libor reform/Discontinuation of Libor etc.)
• Experience of identifying, assessing and monitoring of Non-financial risk
• Hands on experience of building Artificial Intelligence and Machine learning/deep learning models
• Experience of Model Risk Management, Benchmarking, Model as a Service
• Experience of using technology and analytics to design and implement RPA solutions
• Applied Statistics background
• Relevant professional qualifications (e.g. FRM, CQF or CFA) is a plus
• Experience of working with Cloud Technologies
•Use of external data sources (structured/unstructured)

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