Jhonasttan Regalado has worked in various technology roles at bulge-bracket banks since he got his first job leading the massive overhaul of Chase Bank's IT platform during the Y2K programmes at the turn of the century. He's since worked for Nomura, Macquarie and UBS, which he left to join Deutsche Bank, where he's a project manager in its equities business, and, after participating in an NYC Data Science Academy boot camp, is now a fellow there.
A friend in the industry was interviewing at Deutsche Bank for a production support role in equities cash high touch in 2013. He was told his technical skills did not match the role. So he recommended me instead. It is important to treat others with respect, as it is a small world.
I was happy at UBS, but felt a tug to consider the role, as DB was undergoing cultural changes and was in the middle of a migration to a major trading application. I decided to embrace the opportunity and was quickly on-boarded.
As it turns out, many systems were in mid-migration phases, so I dealt with a lot of bureaucracy until these systems-related workflows were fully migrated. I was able to apply core technical skills and grow my management skills in the IT operations role through the different challenges I would face for the next three years.
My responsibilities have grown from developing the equities cash client services model to scaling the model to other production support teams in equities cash, as well as building and developing the talent pipeline.
In my experience, one of the cons of working in IT at a big Wall Street bank is that migrating from legacy environments is a complex process which requires constant planning and quality assurance (QA) feedback from end users. The process is time-consuming but critical in order to avoid major setbacks due to gaps in workflow implementations that result in financial, regulatory or reputational impact to the organization.
A significant pro, for me, is that if you are up to the challenge and have a consistent approach to assessing and understanding issues, learning quickly from mistakes and bringing together a matrix team for problem-solving that leads to solutions, this kind of effort is recognized and rewarded with new responsibilities, promotions and financial bonuses. Your voice matters.
For me, the strength of data science is the use of the scientific method for validating your hypothesis through experimentation. You no longer rely on gut feeling alone to make strategic decisions that impact an organization for many financial quarters or years.
AI and machine learning are helping to codify services traditionally provided by a systems engineer or a financial expert. I believe having a foundation in data Science and machine learning is necessary to improve your ability to problem solve with data and present your ideas, help make sense and align yourself with where jobs are headed, for example, coding, automation and services; and understand how to position yourself within an organization to add value.
Whether as an analyst, data scientist or engineer, the ability to process and explore data, identify patterns and forecast trends are critical skills that help attain employment and expand your career opportunities today.
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