Alexander Beck, PhD, is a data scientist with a demonstrated history of utilizing machine learning and data science in the financial sector, especially asset management. Alexander has a 10-year track record in business applicable artificial intelligence research, including in the fields of financial markets and customer analytics.
Data scientists analyze and interpret mountains of complex digital data to inform decision-making and strategic processes. When it comes to digital procurement and supply chains, data scientists can automate workflows and employ predictive analytics to more accurately forecast demand or disruption.
IBM predicts that demand for data scientists will increase by 28 percent by 2020, with Finance and Insurance, Professional Services and IT generating the most demand. This role often requires an advanced degree, such as a master’s or PhD. For those who are looking to add data scientists to their teams or want to learn how to best work with data scientists, Alexander shares insight into his function, how he assists the business to make informed decisions and automate workflows, and highlights some common misconceptions about data scientists.
Can you talk about your background and education--how did you get involved in data science?
I am a physicist and hold a PhD in economics. Through my education in physics, I learned all the nuts and bolts for working with data. Stochastic calculus is thoroughly taught in physics as well as programming. Here, I have a focus on Python which is a fantastic tool to bring data science to life. My PhD was in close cooperation with a quantitative hedge fund where I was trained to utilize data science to generate economic value.
What is a typical day like for you?
It is a mixture of looking after my team, discussing open topics and questions with them, and meeting with the business side to make sure that we stay well aligned and focused. I especially enjoy our deep-dive, problem-solving discussions where we need to come up with creative solutions. Since data science is not intuitive for everybody, explaining and educating how and why we do the things the way we do them is key. As a consultant, my day also has a solid sales component.
What is the main objective of your role as a data scientist and what kinds of projects do you work on?
My objective is to utilize data to automate workflows and decisions. At the end of the day, it should make my clients’ lives easier, smoother and more profitable. It should enable them to focus on the tasks that cannot be solved by data science and there are usually still plenty. My clients are mostly asset managers and hedge funds in which I show them ways to utilize artificial intelligence in their investment processes. There are plenty of possibilities, ranging from AI-driven investment strategies to AI-optimized order execution.
What advice do you have for those who are interested in a career in data science? What are the technical skills needed for a data scientist to be successful?
You should be fluent in the basic skills, which are programming (ideally Python or R), working with data and statistics. Without that, you’ll have a tough time getting to the point where you can produce an actual value as a data scientist.
What are the non-technical skills needed for a data scientist to be successful?
It is very important to understand the business mechanics in the field where you are working. This allows you to identify the valuable levers to attack with data science and will generate the attention on the business side you will need to succeed as a data scientist. You need to be a good listener and be able to explain and demonstrate your work in a way that non-data scientists can understand it.
How has machine learning and artificial intelligence impacted the financial services industry?
If you look closely, it’s in fact nothing new for the financial services industry. Many institutions tried to utilize neural networks (a machine learning method) in the 90s and have not made good experiences. Many are giving AI a new chance and I believe that today that things have changed a little.
The expectations are more realistic and the approaches are more conservative and professional. On top, the application of AI and machine learning has become much easier and as such projects can focus more on the value generation and less on the nitty-gritty mathematical details of machine learning. The institutions I talk to these days are either about to adopt AI in first projects or already have specialized solutions up and running. The maximum degree of impact is still ahead of us since regulations are still positioned rather conservative toward AI and this means that AI cannot be utilized in all places where it would make sense.
What we will most likely see in the near future is that tasks with a high degree of repetition and small business impact will be automated by smart algorithms. One example is order execution, which was a people business based on personal trust some 10 years ago and is today almost exclusively automated.
Many people worry that artificial intelligence and machine learning will replace human workers. What are your thoughts on that?
In fact, in many occasions what people really mean is digitalization. Think of the travel industry, where a huge portion of bookings happen online today and no longer in travel offices. Still, travel offices exist and are in business and things obviously have changed, but not necessarily for the bad. Ideally, AI can take the role of a vacuum cleaner or washing machine in that it automates that kind of work that you wouldn’t have wanted to do day in and day out anyway. This frees up time and energy for human workers to focus on the tasks in which they can have a meaningful impact and I am convinced that there are manifold ways to achieve this. In fact, machine learning is suffering from an extraordinary hype these days, which naturally confuses people, but I believe that the real-world impact is not as huge as the hype suggests.
What excites you about the future of data science, artificial intelligence and machine learning?
On the one hand, it modernizes existing businesses with automated decision-making, but even more, it opens up completely new possibilities such as self-driving cars that have the potential to completely change our way of living. Just imagine the street in front of your house with no cars.
How do you see the role of data scientists evolving in the next 10 or 20 years?
The data science tools and software are evolving and the degree of automation is increasing here as well. This means that it gets easier and easier to generate models and put them in action. At the same time, the flood of data is increasing, and I have seen almost no company that really deals with their data in a future-proof way. This means that managing data will remain to be an extremely important task, whereas the pure machine learning application part will not be as exciting as you might think (leaving research projects aside). Data scientists will need to be good business people and demonstrate and sell their value.
Are there any notable or emerging trends that you currently see in the industry?
Talent is scarce, but there are so many courses available now at universities around the globe that junior data scientists will not be a scarce resource for long. What’s missing are the experiences of people who are not only data scientists but also team leaders, salespeople and managers.
What do you wish more people knew about data scientists and their role in an organization?
Data science can and will deliver real value but it’s usually not happening immediately. Hence, an organization needs to understand that data science is not magic but hard work and that success will come eventually. I once started as head of data analytics at a FinTech company and my job for the first three months was to set up a proper data foundation. It was hard for me to explain to the CEO why I couldn’t surprise him with unexpected insights on my second day (I am exaggerating a little but the core of the message is true).
Stacy Mendoza is a Digital Marketing Specialist with Sourcing Industry Group (SIG). Stacy began her career in market research as an editor for Hart Research Associates in Washington, D.C. Since moving back to Florida in 2014, she has worked in marketing and public relations, specializing in content creation, media relations and crisis communications. Stacy is a passionate volunteer who donates her time to help nonprofits develop marketing strategies and awareness campaigns. Stacy holds a Bachelor of Arts degree in English from The Florida State University in Tallahassee, Florida. Follow her on Twitter and tweet at @SIG_Stacy.