Joseph Lee: 6 Years after the Bootcamp
The skills the interviewee mentioned can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Could you tell us a little bit about yourself and your background?
My name is Joseph Lee, I am currently one of the co-founders and Chief Data Scientist of ReAlpha.ai. We are a startup that focuses on developing a real estate platform to help identify mispriced assets and help revenue management. We are developing a platform where potential investors can scroll and make decisions. I am using data for their real estate assets.
Can you talk a bit about your educational background and work background prior to enrolling in the Bootcamp in 2015?
I took a Machine Learning course during my last year at Northwestern University. I enjoyed the material and I wanted to see what I could do to corporate data-driven insights, specifically machine learning, in my career. After some online due diligence, I discovered that Data Science was on the rise. Despite the heavy reliance on Machine Learning, it is important the fact of owning what you develop and being responsible for what you make. Due to my inexperience with scripting languages and actual applied machine learning, I realized that I wasn’t well equipped for breaking into an entry role.
I have developed strong fundamentals in computer science concepts, I was familiar with Java and C languages. However, Python and R were some languages I was a little weak on. Thus, I did a little more research and I decided to pursue my education in data science by kicking it off at the NYC Data Science Academy.
What inspired you to pursue data science and sign up for the Bootcamp at NYC Data Science Academy?
The best choice would be the curriculum being focused on R and Python. Being able to try and learn skills that I see myself developing in the future. The program emphasized breadth, which was also what helped me achieve my first role as a Data Scientist.
Can you tell us about how you acquired your job at Uptake after completing the Bootcamp?
Near the end of the Bootcamp, I applied to a variety of entry-level data science roles primarily in New York City and in Chicago. A decent amount of tech startups reach out to me as they saw my portfolio with the products that I developed at the NYC Data Science Academy. After interviewing with them, there was a strong alignment in what kind of experiences they could offer me as a grown data scientist at Uptake. Eventually, it lead me to accept my first offer at Uptake as a Data Scientist.
How did the Bootcamp help you qualify for the Berkeley Master of Data Science Program?
The Bootcamp helped a lot, especially in two things. First of all, it helped me develop my skillset using Python and R. Secondly, an asset for applying to these programs is to have working experience. Thus, having taken the Bootcamp at the NYCDSA and also having work experience at Uptake, gave me the opportunity to have a “jumpstart”. At the same time, the experience I gained helped me skip a few intro courses and I was able to load up earlier on more interesting and advanced courses such as Data Ethics and courses such as Advanced NLP/Deep learning.
How would you compare the two programs? Any overlapping portions?
There is a very decent overlap. It was a keen to my application the fact that the NYCDSA has a broad functionally breadth-focused curriculum and it gives those who are not familiar with data science the exposure to all the different areas they can grow. I noticed overlaps especially with courses at UC Berkeley that focused on Machine Learning, Python applications, Data Analysis, and Visualization.
However, since Berkeley has a longer program. What was not covered in the overlap, would be more of the advanced courses such as Data Ethics, Natural language processing, Scalable, Data Engineering as well as Machine Learning Practices. Due to this overlap, I was able to prepare well for the earlier classes and I had the opportunity to succeed in my more advanced courses.
Did Bootcamp experience help you do better in Berkeley regarding the chance to be admitted and in Academic performance?
From my personal experience, the Bootcamp was certainly an asset in providing evidence of my skill set as well as helping me really not only be admitted but to also pursue courses that truly interested me and that I believe benefit would my career.
Which skills taught at NYC Data Science Academy did you draw on most for the job?
Uptake focuses on IoT (Internet-Of-Things Analytics) in which they provide data science-powered maintenance services for anything that is machine assets such as tanks, railroads, and locomotives. I was starting out as an Associate Data Scientist and I had the opportunity of joining our initial modeling efforts and helping to deliver them.
I was able to succeed in these roles because the NYCDSA taught me how to run
Machine Learning applications of Python and R, which are the two of the most heavily used languages at the core of the team at Uptake. Having that breadth allowed me to work on a variety of projects. In addition, the concepts of machine learning model tuning and model trade-offs were also important in giving me the foundational core skill set to begin working with little overhead. This helped me to also engage with more experienced data scientists, take me under their wing, and show me ways to grow my skill set.
What was your motive in moving on from Uptake to McKinsey?
The motive was purely out of “selfishness”. I always had a passion for Finance and an interest in working and connecting in New York City. I have always thought that the areas within finance and data science could definitely disrupt, innovate and provide a lift. Lots of things aligned when I was interviewing with McKinsey.
Ultimately, I accepted the role to join the Financial Services Laboratory at McKinsey due to their focus on applying data science and solution engineering to financial clients, but also to open an opportunity to really explore New York City. Later in my career, I realized that it's one thing to be really good at the development of the modeling but it is also very important as a data scientist to own what you make, and ethically marketing it, and selling it for the right reason.
What was your main focus working at Revantage/Blackstone?
During my time at Revantage in Blackstone, I focused primarily on delivering machine learning and data services to portfolio companies. I primarily worked in luxury resorts and hotels, as well as some experience in multifamily living portfolio companies in which I would work closely with their Executives in ideating solutions that can use the Revantage data platform to provide new quantum mental insights for their Investments.
Our main question during the time of Covid-19 was “How do we strategize for our recovery?”. The problems we worked on were exclusively for leadership. We helped them thinking of new strategies and solutions for recovering and for dealing with the post covid world.
Was founding your own company always your ultimate goal?
Yes, I was always interested in pursuing a role with data-driven leadership and in tackling bigger problems with leading the team in mind. In achieving this role, there was a little bit of luck and a little bit of reaping the fruits of building a network of connections in New York early on. First of which, given Covid-19, more attention has been given to real estate and therefore there are more DC's, more firms that are interested in seeing what we can do to improve and disrupt real estate; making it robust for another Black Swan event.
In addition to that, my experience at McKinsey and in Blackstone has helped me to make connections between real estate which tends to be a pretty close-off box. As everything converged the pandemic, I was later approached by interested parties who liked my background. Through word of mouth, they knew my abilities in delivering data solutions regardless of the industry. After discussing with them, there were a lot of things that we agreed on which ultimately led to them creating a new role for me as a Chief Data Scientist to lead and build their real estate pipeline.
What do you look for in a job candidate?
We are actively hiring data scientists and data engineers. We are interested in candidates that are interested in taking ownership, interested in developing skills set so that within a short amount of time, they can operate with minimal overhead. At the same time, they are able to pride what they own and continue to take it to the next level. As of now, we are focusing a little more on data engineering due to the nature of our products, in that it's being very data-centric. For example, our top priorities are data warehousing and data curation.
As for soft skills, we are looking for candidates that have a good understanding of data science technologies but ultimately have a scrappy mentality. Since ReApha.ai is a start-up, there are so many hats to wear. We have tremendous opportunities for new candidates that really grow and build their career. However, since we're not an established Corporation at this moment, we are looking for those who don't mind really just pushing up their sleeves and wearing multiple hats in order to get their project done.
Have you hired NYC Data Science Academy Alumni?
Yes, we have. A couple of weeks ago, we gave our first Lead Data Engineer role offer to Paul Grech, who is an NYCDSA alumnus and who also was my cohort mate during my attendance at the NYCDSA. You never really know when your Bootcamp relationships actually end up in a fruitful working relationship in the future.
Can you tell us about some volunteer work you have done?
The philosophy of machine learning is focused on having an ensemble of different opinions and different data sources is very important when it comes to Inclusion and Diversity in Data Science.
During my time at the NYCDSA, I was always interested in tutoring, mentoring, offering guidance, and working with the NYCDSA as an example. It was very inspiring, thus, during my first role at Uptake, I took the inspiration with me and I participated in a lot of Uptake’s social projects.
Uptake has partnerships with social justice firms and nonprofit organizations, in which we volunteer our time to offer guidance and mentorship on how to use data. One of these organizations that I came across was Black Girls Code and through a coworker at Uptake, I discovered that one summer they had a shortage of volunteers.
After signing up and participating, I was hooked on their mission and in what they're doing. It is important as I believe data scientists whose models can have a huge impact on the economy in the US. It is important to give back to your community and allow the next generation, especially women and people of color, to give them the resources and opportunity to break the cycle and proverbial ceiling, to have them advance in their careers.
I believe data science can offer levels of opportunities and I was very fortunate enough to have NYCDSA to inspire me; as well as Uptake, providing a platform for me to be exposed to all of these very likely impact organizations. I highly encourage any data scientists who have time or even ask the organization they belong to about what is their policy for assisting nonprofits or volunteering their time because at the end of the day we are all one community and we need to support each other when we can.