Meytier was honored to interview Eric Steinhoff, EVP- Client Impact at Scienaptic. Scienaptic has worked with Meytier over the years and they continue to impress us with their passion for their work, their clients, and their commitment to growth. We're sure you'll be just as inspired as we were with Eric's thoughtful responses and insights into data science, its future, and its practitioners.
Tell us a little bit about yourself, your professional journey and how you came to be where you are now.
I’ve been in various credit risk management roles over the last 23 years. I started out at Capital One, where I worked in product strategy, credit strategy and then a more general centralized credit risk management function. I left Capital One after about 10 years and moved to London and then to Johannesburg, South Africa where I was Head of Credit for Standard Bank's Auto Loan portfolio. I came back to the U.S about 10 years ago and started working for smaller lenders then startups. Eventually I connected with Scienaptic. I’ve gone away from big banks, I very much like working for smaller companies. I like the amount of impact and influence you can have at a smaller company. The cutting-edge work in the lending space that Scienaptic is doing is what drew me in and resonated with me. I could see myself adding value to what they were doing and really being successful here.
Data Science and AI are in such high demand and more companies are investing in this space. How should companies think about extracting the full value from Data Science?
Data Science is all I really know in terms of how to make strategic decisions. Starting out at Capital One back in the late 90s, we called it “Information Based Strategy”, which was really a precursor to data science. The whole premise of Capital One’s decision-making approach was looking at all data you have available to help you come to an informed decision. Whether that be in terms of market segmentations, product strategy, credit policy, etc.
I think so many have been willing to embrace data science more recently simply because of the plethora of data available. In terms of how companies can extract the most value, most people don't realize this until it's too late, but it starts with strong collection and retention of data. It's great to hire a team of data scientists, but they need strong data to work with. Data-informed decisions and data collection need to be embraced as part of the culture of the company. We are a data-based decision-making company. We use data science to make better decisions. When there is a lack of data then obviously you can’t. Of course, when there is not enough data, you still often have to make a decision. I’m not saying data is the only way, I’m just saying it’s a mindset. It gets built into the culture and ethos of a company.
What is Scienaptic doing that is driving the demand for data scientists in your organization?
We are a machine learning and AI based credit decisioning engine. Our software platform really functions as the brains of a lending company. When a lender takes a credit application from someone, and they pull credit bureau information, call on other third-party data providers, or ask them to connect to a bank account or provide proof of income, they are collecting that data and incorporating it into some sort of decisioning mechanism. Some of that is they have human beings underwriting, for example, lending in the small business space is still pretty heavily human powered decisioning. But what Scienaptic does is really codify and systematize those credit underwriting decisions that a lender needs to make. By doing that, we enable the lender to make better decisions by empowering them with AI and machine learning tools that go above and beyond what lenders have traditionally used.
When we work with a client who is trying to move their credit decisioning engine onto our platform, we are not just giving them the keys to the platform and telling them to go figure it out, we’re helping. We work very closely with them and help analyze their data. In many cases we build them a custom credit risk scorecard that leverages machine learning techniques. It’s almost like a hybrid approach where we are selling them software but we are also providing them advisory services. It is a very collaborative approach to getting the software deployed. In that, we’re using our growing team of data scientists to make informed recommendations to our clients so that they can get the most out of not just our platform, but also data they have in house that they may not be fully utilizing.
Our client base runs the full gamut from smaller credit unions to large banks. Large banks obviously have data science teams in house but a lot of our smaller clients don’t. So we help them supplement whatever needs they have to analyze their data and make data-based strategy recommendations. We are a startup, and startups either grow or die. We are growing very rapidly. As our business grows, we will continue to have lots of roles to fill for data scientists.
There has been a lot of industry spotlight on ensuring that the use of AI is “unbiased” and this is even more important in areas such as lending. Are there any specific strategies and initiatives that you take to ensure minimal bias?
We have to ensure that our models are unbiased not just because it’s the right thing to do, but because if they aren’t, regulators will be knocking on our door. Regulators require documentation from lenders to prove that models are not biased or exhibiting bias in who gets approved. That goes as far as not just direct, overt bias, but also implicit bias. It’s a concept called disparate impact, where even if your model doesn’t look at variables or attributes that would potentially indicate bias, like age, nationality or race, it could be implicitly creating a bias. Regulators require lenders to prove that their models aren’t creating that disparate impact and disadvantaging certain groups.
That said, because they’re based on data, all models reflect history. Companies retain a lot of data over the years, and our models are based on the data that has been provided. What we bring to the table to help combat potential bias is our experience having worked with lenders across different spectrums, different products, and different types of lending approaches.
Because we have such a breadth of experience with different data sources, we’re able to help make better decisions. We come from a standpoint where we know how to approach a model building project so that it is less likely to provide bias than it would be if we just relied on historical data provided by that lender. That’s a value that we take great pride in. We approach every modelling project with a mindset to avoid bias not just because it is a requirement, but because it’s the right thing to do. Our CEO talks frequently about wanting to leverage machine learning and AI logic to democratize credit and bring credit much more broadly than it has been historically.
How do you hire? What do you look for in data science professionals?
We start with traditional tools like resumes, experience, and education. Then, what Scienaptic does differently is go through case study focused and behavioral type interviews. What we are really looking for is problem solving skills. I came up in an environment where data is used to inform better decisions. So what we’re looking for in an individual isn’t just someone who can apply tools they were taught in school or apply an algorithm, but can they identify what the real world problem or issue is? Can they then come up with a solution that could be executed in the real world? It’s a real world problem-solving approach versus a purely technical approach. We value that pragmatic problem solving angle more than the pure technical skills. With COVID we are working in completely different locations. We have offices in two different countries, in India and in the US. I’m in Dallas but we have US offices in New York. We have people who worked from home even pre-COVID. That is to say, we are a self-motivated company, and value candidates who are independent workers that can get things done. To be able to execute a task without someone looking over your shoulder and checking in constantly is an incredibly valuable skill for a startup like ours.
What advice would you give to younger people looking to build a career in this space?
First and foremost, we generally don’t hire fresh out of university, most of the candidates I hire have some experience. Early in your career it’s important to focus on the technical skills. I know I was just talking about real-world problem solving, but you need a strong technical foundation and the best time to cultivate that is earlier in your career. As you progress in your career, you’ll have a lot less time to dedicate to building those technical skills, building skills early on is going to be of huge value to you down the line.
My next advice is to decide which specialty you want to work in. Data science is a very broad term, not all data scientists are created equal and not all can do the same kind of thing. There are very narrow fields in Data Science, and there are more general problem-solving people who leverage data to make better business decisions. Try to figure out what your interest is and what specialty you want to focus on. It could be data engineering, coding, client facing or problem solving, etc. Figuring that early can help you greatly, especially in building industry-relevant skills and domain knowledge. That doesn’t mean that you can’t jump industries, it just means once you have experience in an industry, your opportunities are probably going to be the best there.