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Credit Data Analytics Lead

Credit Data Analytics Lead

  • Location Hungary - Central - Budapest, Hungary - Csongrád - Szeged
  • Travel required Negotiable
  • Job category Finance Group
  • Relocation available No
  • Job type Professionals
  • Job code 132566BR
  • Experience level Senior
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Job summary

Would you like to discover how our diverse, hardworking people are leading the way in making energy cleaner and better – and how you can play your part in our world-class team?

Join our Trading & Shipping Finance team as a Credit Data & Analytics Lead



Our finance professionals play a vital role in creating value and managing risk as we reimagine energy. As trusted advisors, finance is transforming how the business plans and performs. Join us to be part of an expert, integrated team generating value across bp!

As a
Credit Data & Analytics Lead you will become an active member of the extended global Credit team within bp’s Trading & Shipping business. Your key focus will be to help develop quantitative models and reporting tools and you will be given an opportunity to play a key role on a challenging high profile multi-million-dollar systems roadmap. You will also be at the cutting edge of data exploration activities in order to help drive efficiencies via automated tools, provide valuable insights as well as tackle business problems using data.


In this role, you will:
  • Contribute to the development of current and new Credit MI, metrics, dashboards and decision tools in order to gain accurate and timely insight into bp’s Credit Risk drivers
  • Create, implement, and support credit risk quantitative models for the trading business leveraging a wide variety of mathematical and computer science methods and tools including C++, C#, Java, object oriented software design, Python, Structured Query Language (SQL), mathematical finance/ programming and statistics and probability
  • Support various technology and systems initiatives
  • Develop strong understanding of bp Credit Risk frameworks as well as acquire strong commercial awareness
  • Develop knowledge to build algorithms and design experiments to merge, manage, interrogate and extract data to supply tailored reports to Credit function, bp Senior Leadership Team or the wider organisation
  • Understand and utilise machine learning tools and statistical techniques to produce solutions to problems
  • Maintain clear and coherent communication, both verbal and written, to understand data needs and report results
  • Remain up to date with the latest technology, techniques and methods
  • Stay curious and enthusiastic about exploring new risk modelling concepts, algorithms and data exploration methods to solve problems and enthuse others to see the benefit of your work



In this role we have the following requirements:
  • Strong academic background including at least a Bachelor's degree (Computational Finance, Mathematics, Engineering, Statistics or Physics preferred) or equivalent
  • Knowledge of commodities/energy credit risk exposure modelling concepts and credit pricing
  • Knowledge of Probability of Default (PD) / Loss Given Default (LGD) / Exposure at Default (EAD)/ PFE modelling
  • Experience in any of the following software development environments: Python / VBA / Java / C++/ C#/SQL
  • Fluency in English
  • Prior exposure to energy commodity trading, credit risk/quant roles is an advantage
  • Effective listening skills in order to understand business requirements
  • Team-working skills and a collaborative approach to sharing ideas and finding solutions
  • Drive and resilience to try new ideas
  • Comfort working with ambiguity, framing and solving problems in an iterative manner
  • Positive attitude and curious mind
  • Commercial experience using machine learning techniques is an advantage
  • Experience with using Numerix and Dataiku platforms is also an advantage


We are an equal opportunity employer and value diversity at our company. bp Hungary won the Most attractive employer 2021 Award, based on the PwC annual research. Come and join us!


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