Executive Interview: Efe Ogolo, Lead Data Scientist, HelloFresh

Tell me a bit about yourself. How did you get to where you are now? What interests you about data science?

During my undergrad, I had a great opportunity to intern at a financial institution out in Edmonton and the internship was within their credit analytics department. So that was kind of like my first intro into the world of data, learning about the world of advanced analytics. I was helping with things like; data gathering, data cleaning, things like that. I was working very closely and was exposed to some of the very interesting work that the Bank was doing, so it definitely piqued my interest and got me excited about the space. I spent almost two years at the bank and after, I was pretty dead set going into the data space and that’s what led me back to school.

I did a graduate degree in the analytics space and then after my graduate degree was fortunate to get a job here in Toronto on a data science team that was just getting established as a tech company called Soft Choice. Shortly after that, the HelloFresh opportunity came up and I’ve been with them ever since.

What is your current day today is like and is there anything exciting that you’re working on?

My current day-to-day involves leading our data science and advanced analytics team here at HelloFresh Canada. My day-to-day is a mixture of a few things, there are meetings with my internal team, and then meetings with different stakeholders to continually provide updates on the progress of different projects.

Currently, what project are you most excited about?

We are currently trying to leverage some deep learning methodologies to better understand what our customers are saying about us, how our customers are feeling about us and about our products, and things like that across, various public forums that are out there for people to leave reviews and voice their comments. We are exploring that space and figuring out how we can most effectively use those insights.

What are some of the trends you’re noticing within your industry?

The meal-kit space is still a relatively young space. That being said, there are a lot of players out there. With COVID, I think we’ve seen more people being open to trying the idea of a meal kit, so I think there’s been an interesting exposure of this idea to people who may not have heard of it before. We’re also seeing a lot of people leveraging more of the discounts we offer.

What is your greatest challenge right now what is keeping you up at night?

I think like any other company, data quality is one of utmost importance, as the adage goes, garbage in, garbage out, right?

Most companies want to make sure that the data they’re collecting and gathering from different aspects of customer transactions and interactions, that the data is clean and that it is of the utmost quality. That way when we go to perform these different analyses, we can come up with insights and say that this is accurate. Whatever we portray to our stakeholders within the business, we can be sure that we’re giving them the right insights because the confidence in our data quality is there. It’s also making sure that our data governance, our data management, and all those systems are the best as can be because every insight that will direct us will be very dependent on the quality.

How are you trying to solve the data quality issue?

We’re making sure that we have the right data infrastructure and tools as data literacy can impact the entire business. We want to make sure that people understand what they’re looking at and that they know how to go about slicing the data. They know where the data is coming from, they know under what types of contexts and business rules the data is being generated out of so that whenever they’re coming up with an idea, they understand the context around it which is very important to whatever insights that you draw from data

What do you think of the current state of governance and ethics when it comes to AI and ML?

I think this is a very interesting area. There are many conversations happening now in the larger community in terms of things around ethics. There are different algorithms that different large companies are using that are not under fire because of the different biases and things like that are embedded in this algorithm, so I think it’s one that a lot of work still has to be done.

Machine learning or AI solutions are not the problems, they’re only a product of whatever data they were trained on. We want to make sure that when it comes to whatever output, whatever industry it is if we’re trying to make decisions, we want to make sure that we are trying to control for any bias that may negatively affect any subset of the population or anybody in a negative way and an unintended way. I think those are conversations that the data science teams need to continue having, that we make sure that whatever data points that we’re using, that is training these models are as representative and non-biased as possible.

How will emerging tech play a key role in the development and evolution of Hello Fresh’s services?

I think for the last year or two, the idea of data science has been built around the concept of building tools and technologies that allow everyone and anyone in an organization to be able to leverage some of the power of a data science algorithm. A lot of companies are moving in that direction. At HelloFresh, we want to be as data-driven as possible, so we want to make sure that everyone within our organization can work well with data and make accurate and insightful business decisions based on it. I think the rise in tools and technologies has a kind of democratized data analysis and democratized data expertise. I think that will continue to help HelloFresh move forward in the future.

Are there any last words or topics, you think the public needs more awareness around?

Data is very powerful, there’s a wealth of information and there’s a lot of insights that a lot of times is low-hanging fruit we can gather information from. There is a perception that data science is a very mysterious thing and that it’s very difficult. There is this fear but at the very bare minimum is descriptive analytics where you can still gain some pretty interesting insights that you can use to move your organization forward. There are a lot of insights that can be learned from doing very elementary analyses of descriptive analyses, or simple analyses of the data that you are collecting. It’s important for organizations to move towards making sure that they‘re collecting and tracking as many things as they can and then applying some very simple analyses techniques to retrieve actionable insights.