J.P. Morgan Vice President, Data Scientist Lead
Institut national polytechnique de Toulouse Doctor of Philosophy (Ph.D.), Signal Processing
Current Time 0:00
/
Duration Time -:-
Progress: NaN%

How did you get to where you are today? What is your story? What incidents and experiences shaped your career path?

Summarized By: Jeff Musk on Sat Jan 11 2020
Starting from my PhD. I finished my PhD four years ago in France. My research topic is about Inverse problems in image processing on machine learning and after that, I did a one-year postdoc in the University of Cambridge. And then I moved to the US Duke University for one year postdoc. And then I finished my career in academia then moved to industry to Simmons Research in Princeton for one year. And since last year I joined JP Morgan AI research. That's basically how I arrived here today. 

What are the responsibilities and decisions that you handle at work? Discuss weekly hours you spend in the office, for work travel, and working from home.

Based on experience at: Vice President, Data Scientist Lead, J.P. Morgan
Summarized By: Jeff Musk on Sat Jan 11 2020
So basically here we are trying to automate Machine Learning for various problems, financial problems here in a bank. Data Scientists are trying to design different Machine Learning models on time series forecasting problems. I travel basically for conferences twice a year and each time, one week. I mostly work from office. I seldom work from home. Maybe once a month. Yeah. So every day for eight hours I'm in the office.

What tools (software programs, frameworks, models, algorithms, languages) do you use at work? Do you prefer certain tools more than the others? Why?

Based on experience at: Vice President, Data Scientist Lead, J.P. Morgan
Summarized By: Jeff Musk on Sat Jan 11 2020
Currently, the programming language I'm more comfortable is Python and models most its internal. Models? all classical regression models. For different tools. maybe I prefer TensorFlow and PyTorch. Currently, we don't have a preference for deep learning models as we're trying to check whether it brings some benefits. For example, I have used some temporal convolutional networks. So this network comes from speed processing area of years ago. 

What things do you like about your job? Were there any pleasant surprises?

Based on experience at: Vice President, Data Scientist Lead, J.P. Morgan
Summarized By: Jeff Musk on Sat Jan 11 2020
Things that I like here is you have very practical business problems. You have massive real data. If you follow the policies, you can try to leverage that. And you have people from different backgrounds. With them, you can work as a team and you can learn from each other.

What are the job titles of people you routinely work with inside and outside of your organization? What approaches do you find to be effective in working with them?

Based on experience at: Vice President, Data Scientist Lead, J.P. Morgan
Summarized By: Jeff Musk on Sat Jan 11 2020
The career path here is analyst, associate, VP, ED executive director, and the MD, managing director. And some C suite like CEO.  

What major challenges do you face in your job and how do you handle them? Can you discuss a few accomplishments?

Based on experience at: Vice President, Data Scientist Lead, J.P. Morgan
Summarized By: Jeff Musk on Sat Jan 11 2020
One major challenge is you have to formulate a business problem into a scientific problem in which you can leverage your expertise to solve it. Um, yeah. For example, for some financial data. I cannot talk about details like to predict the stock price, you have to collect a lot of data. And the data is kind of dirty. You have to do a lot of processing and to do the processing, you need domain knowledge, which you don't have. So you have to talk with business people or financial people to quickly learn the domain knowledge to do the processing. That's something we have accomplished. This is a major challenge.

What are the recent developments in the field? How significant are these improvements over past work? What are their implications for future research & industry applications, if any?

Based on experience at: Vice President, Data Scientist Lead, J.P. Morgan
Summarized By: Jeff Musk on Sat Jan 11 2020
Time-series forecasting, applying machine deep learning techniques is kind of new. A similar area is NLP, natural language processing. This area has been explored intensively. Time-series forecasting applying, for example, LSTM or CNN or temporal convolutional networks are kinds of new. The improvements currently I have seen are in the theoretical face but in the product, I don't see too many new models have landed. I think in the future, there are things for which you have a bunch of theory and models, there is also grounded products based on these. That might be in the future.

What was the hiring process like for your job? What were the roles of people who interviewed you? What kind of questions were asked?

Based on experience at: Vice President, Data Scientist Lead, J.P. Morgan
Summarized By: Jeff Musk on Sat Jan 11 2020
The hiring process was I had two phone interviews before they bring you on-site. The people who interviewed me were almost everybody on the team. And one or two people from some other groups. There are three classes of questions. Technical questions related to your background, research background or working experience. The second is more related to what they are doing, what's your opinion or your experience? And the third one is more on behavior. 

What qualities does your team look for while hiring? How does your team interview candidates?

Based on experience at: Vice President, Data Scientist Lead, J.P. Morgan
Summarized By: Jeff Musk on Sat Jan 11 2020
For the interview, they test your coding ability, programming whether you can code in a programing language like Python efficiently and I think for PhD or researchers more, they want to check what is your ability to problem-solving problem formulation and whether you can transform this problem to scientific problem an efficient way.

What are different entry-level jobs and subsequent job pathways that can lead students to a position such as yours?

Based on experience at: Vice President, Data Scientist Lead, J.P. Morgan
Summarized By: Jeff Musk on Sat Jan 11 2020
Entry-level jobs for new graduate students is an internship. If you perform well, we give you a job offer. You can start your career here. Or there are many campuses recruiting every year before the end of the semester. That's also an opportunity to get on board. Any entry-level is an associate. And then after three years. If you perform, where every year you have a review, you will have a chance to be promoted to the VP, and then to ED and that depends on case by case. 

What were the responsibilities and decisions that you handled at work? Discuss weekly hours you spent in the office, for work travel, and working from home.

Based on experience at: Research Scientist, Siemens
Summarized By: Jeff Musk on Sat Jan 11 2020
That was more research-oriented. We didn't deliver products. We made some prototypes. Basically, you read the papers you extract interesting ideas, implement them or other than that propose some new ideas, to the team. And then you talk up with unions whether it's possible to bring these to the product. Working hours were similar to here. I didn't travel for conferences either.