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Not all banks are ready for modern data science.

Why quants quit jobs in investment banks

If you work for an investment bank and you want to see your colleagues in quant jobs bristle with barely controlled exasperation, it turns out there are easy buttons to push. You just need to give them a complex task and ill-equip them for solving it. 

So said Daniel Rosengarten, head of asset liability management (ALM) quantitative development at Barclays, speaking at this week's conference on AI and data science in trading in London. "If you want to see a quant go crazy, give them a large amount of data, and Excel to work with it," said Rosengarten.

Too few banks actually equip quants with contemporary data management tools like Spark and Jupyter, said Rosengarten. "I've seen banks that have said they'll have to upgrade their Excel spreadsheets because they're still using Excel 2003 or 2007." Instead of using the newest tools there's a tendency to assign 'spare' quant talent to lengthy data analysis tasks using old technologies, Rosengarten said.

This has a predictable outcome. "I’ve been in quant groups where...people couldn’t work on any interesting projects, so they basically left and went back to college," Rosengarten added.

A related problem is banks' poor appreciation of the fact that before data science solutions and machine learning can be applied in finance, data needs to be cleaned. This is neither sexy nor a priority. "Cleaning the data is 80% to 90% of the work and most people don’t feel like doing that," said Apurv Jain, a visiting researcher at Harvard University, speaking at the same conference.

Too many banks have "data swamps" instead of data lakes, agreed Rosengarten. "In the successful projects I’ve seen, the data cleaning takes years." People need to clean the data, anonymize the data and to understand its value, said Rosengarten. Without this, nothing can be achieved. 

If quants working on data projects in banks are to be kept happy, Rosengarten said there are two simple rules: start small and actually complete a project from start to finish.

Not all banks are capable of this. And not all banks are ready to ready to play in the top ranks of data science. John Ashley, director, global financial services strategy at chip-maker NVIDIA, said only a handful of banks have made the kinds of $10m+ investments in hardware that really enable the application of machine learning. "If you want to hire the best data scientists you want to give them good tools – you don’t want to hire Michelangelo and give him crayons," Ashley said. "Unless, you want a crayon drawing of course."

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AUTHORSarah Butcher Global Editor
  • Jo
    Johnny Yiseongjoon
    19 September 2019

    Hi Sarah,

    I have a question. When you say we need to provide quants with great tools rather than Excel, what are some of the examples? Python with codes that can take care of data cleaning?

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