Quantitative Research - Commodities Business Intelligence Group - Associate or Vice President

Greater London
Full time
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JP Morgan
Banking, investment & finance
10,001+ employees
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If you are passionate, curious and ready to make an impact, we are looking for you.

Quantitative Research (QR) is an expert quantitative modeling group in J.P. Morgan, as well as a leader in financial engineering, data analytics, statistical modeling and portfolio management. As a global team, QR partners with traders, marketers and risk managers across all products and regions, contributes to sales and client interaction, product innovation, valuation and risk management, inventory and portfolio optimization, electronic trading and market making, and appropriate financial risk controls.

Job summary:

As a Associate or Vice President Data Engineer in Quantitative Research Commodities Business Intelligence team, you will be partnering closely with the data scientists and quantitative researchers to create a seamless platform for the productionization of analytics initiatives. You will be leveraging your skills in data management, pipelines and visualization to generate insights for management/ trading desks. You’ll contribute to the strategic agenda to transform our investment bank into a data-led business and drive change through innovation and business process optimization. Commodities Quantitative Research team develops and maintains mathematical models, methodologies and infrastructure to value and hedge financial transactions involving commodities underlyings (for example prices of energy, agricultural products or base/precious metals.

Job responsibilities:

  • Contribute directly to the business and client franchise; improving management’s visibility of the business by improving access to & analytics of the vast range of data available
  • Provide insights to the trading desk on the different business drivers to enable them to better serve clients or manager their risk
  • Work on a variety of datasets from external exchange data, vendor data, internal proprietary data
  • Work across a range of projects covering: business analysis/reporting to management, trading signals & fundamentals forecasting, more data driven approaches to solve traditional quantitative problems related to pricing and risk management
  • Understand and refine current data architectures to persist critical business data across all business lines
  • Drive standardization across processes and reporting.
  • Suggest and implement controls to improve efficiency & accuracy of existing processes
  • Build on data engineering skills and further develop data science/ML skills through strong collaboration with technology and data scientists & quantitative researchers
  • Proactively identify opportunities for QR to leverage data and analytics to enhance the Commodities business
  • Collaborate with other teams to leverage the work already done by other asset classes

Required qualifications, capabilities, and skills

  • You have strong data engineering skills covering the full spectrum of data ingestion (internal and external data sources), data management principles, databases, exploratory data analysis & data visualization
  • You are proficient in code design more specifically in object oriented programming.
  • You are proficient in python, in particular with packages most used in data analytics (pandas)
  • You have worked with a variety of datasets of different volumes & velocity & the associated databases that can be leveraged (e.g. SQL, KDB)
  • Understanding of data schemas and data structures
  • You have exploratory data analysis skills & data visualization skills (e.g. through apps built with Voila/Dash or Tableau)
  • You have demonstrated an ability to complete data engineering projects end to end – from data sourcing to delivering analytics – in an industry setting
  • You quickly grasp business concepts outside immediate area of expertise and adapt to rapidly changing business needs
  • You’re attentive to detail and easily adaptable
  • You’re enthusiastic about knowledge sharing and collaboration
  • You have strong interpersonal skills – you listen and communicate in a direct, succinct manner

Preferred qualifications, capabilities, and skills

  • You have advanced degree (PhD, MSc or equivalent) in Engineering, Mathematics, Physics, Computer Science, Data Science
  • You demonstrate experience working with financial datasets (e.g. exchange data, tick data, trade data)
  • You have basic understanding of statistics and ML and associated python packages (sklearn and statsmodels)
  • You demonstrate orientation towards careful system and solution design and implementation
  • You have experience in robust testing and verification practices
  • You have knowledge of Commodities markets