Jhonasttan Regalado worked in various technology roles at bulge-bracket banks on wall street before joining Deutsche Bank as a VP, leading their development and implementation of an IT Operations Support framework. He first became interested in data science as a casual interest, taking self-paced courses, but hit a wall with the complexity of topics around Machine Learning.
Rather than enrolling in a multi-year graduate program, he attended a 12-week data science bootcamp at the NYC Data Science Academy. After the intense bootcamp, he immediately saw changes in his quantitative thinking and new opportunities in his work thanks to his deep understanding of data science.
Why did you decide to enroll in the NYC Data Science Academy? When did you do so?
I was introduced to the world of Data Science in 2013 by a Udacity course on Big Data. My attempts to grow my understanding through the MOOC experience hit a wall when I couldn’t make any further progress in my understanding of gradient descent for Machine Learning. I thought about potentially going back to grad school for Data Science to close my knowledge gaps through the traditional classroom experience, but I didn't want to sidetrack my commitment to family (coaching my children’s basketball teams) and community volunteer projects. After having phone calls and face-to-face sessions with instructors and students at the NYC Data Science Academy, I decided to enroll in March for the Bootcamp cohort in the Fall.
Was the experience what you were expecting? What benefits have you gotten from it? Did it help you land your current position and/or help you do your current job better?
The experience was exactly what I was expecting. I returned to work with a new set of skills that include quantitative thinking and application, research, data processing, automation, exploratory visualization, analysis, and presentation. All of these skills are applicable to my new role as an IT Operations Manager for the Trading Floor in the Electronic Trading space.
My team supports systems that include Algorithmic Trading solutions to clients. Thanks to the bootcamp experience and training in statistics, I am very comfortable discussing the behavior of a trading algorithm that is expected to follow a daily volume curve. As well as setup experiments to capture metrics for evidence to prioritize automation projects that reduce manual efforts/costs.
Take us through your career path, including how you got your foot in the door at Chase and Nomura, how your responsibilities evolved at the latter, and an overview of your roles at Macquarie and UBS.
I was fortunate to find work as an IT Consultant for a Y2K project at Chase Bank during my last year as an undergrad at Baruch College. This was my opportunity into the world of IT. As my one year contract was coming to an end, I realized through the job interview process that having a degree would not be enough to land a full-time job at an established financial organization like Chase. The situation led me to sign-up for my first bootcamp. I spent two months immersed in classroom lectures and labs learning Microsoft Desktop and Server side products, attaining my MCSE (Microsoft Certified Systems Engineer) credentials by the end of the program.
It was no easy feat, but I learned then the value of a well-organized bootcamp training and the personal effort required to successfully finish the program with marketable skills. Soon after I was able to find my dream job at Nomura, working as a full-time Windows Systems Administrator for the Equities Trading business.
My passion for learning the business, new technology and building relationships opened doors to critical roles in IT Operations that were aligned with the Trading Desk. This foundation and practice led to opportunities at Macquarie and later at UBS, where I had the managerial support and environment to develop a Client Services model that was aligned with the needs of the business.
Did all of these require similar skills and experience, or did you have to adapt significantly to each one? Did any of those banks offer much on-the-job training, or were you expected to have the knowledge and ability to get the job done from day one?
A willingness to adapt has been key to my career development in the Financial Industry. Each role has built up upon the previous to some degree. My approach has been to first assess my understanding of the role and expectations, the systems, process, and procedures. I then determine if I will need to plan for off-site training to close knowledge gaps. I am fortunate to have started with systems administration, where I learned about servers and network topology. I was working at Nomura in the World Financial Center when the unfortunate 9/11 event occurred. As a result, we had to move all operations to Piscataway NJ, which provided me with first-hand knowledge of BCP (Business Continuity Planning) and hands-on experience on rebuilding a data center. It was through the building of relationships across IT partners that I was provided an opportunity to move into development.
Looking back, I was very naive at the time and said yes to the role, not knowing how painful the learning experience would be. It took me quite some time before I could wrap my head around the fundamentals of Object Oriented Programming. The hands-on experience in the workplace under the supervision of great managers, accompanied with external training, paid off significantly for me. I was fully engaged in a role where constant feedback from the business led to frequent code releases to production, with application support provided by me and my supervisor. I grew my understanding of SDLC (Software Delivery Life Cycle) alongside good project and change management practices. These core skills would prove to be transferable to future roles in IT Operations.
Please describe the recruitment process at Deutsche Bank, including the interviews you did. Also, what was the on-boarding process like? Have you been promoted or taken on more responsibility there since joining?
A friend in the industry was interviewing at DB (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. As you can see, 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 for 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.
What are some of the pros and cons of working in IT at a big Wall Street bank?
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 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 and/or reputational impact to the organization. A significant pro, for me, is that if you are up to the challenge, your 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 (e.g. new responsibilities, promotions, financial bonus). Your voice matters.
How are data science, AI and machine learning transforming banking in general and banking IT specialists’ roles and responsibilities in particular?
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 1) improving your ability to problem solve with data and present your ideas, 2) help make sense and align yourself with where jobs are headed (e.g. coding, automation, services) and 3) understand how to best 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 predict/forecast trends, are critical skills that help attain employment and/or expand career opportunities today.
The upcoming NYCDSA Bootcamp begins on September 18, 2017 in New York City. Candidates can apply here. Questions and inquiries about program offerings can be directed to email@example.com.