Lessons Learned on Building Data Science Products in Multi Disciplinary Teams

The DatSci Awards are taking place on 7th September 2018, this year there are 9 categories in the competition.

Our most prominent and sought-after award is the Award for Data Scientist of the Year which is sponsored by title sponsor Deloitte, Humberto Corona who is a Data Scientist in Zalando is one of five finalists in this category.

We caught up with Humberto to hear his thoughts on working in diverse teams and functions.

For the last three years, I have been working in different data science projects at Zalando, helping our more than 24 million customers find the most relevant assortment that we have. Along the way, I have learned how to scale data science, or how to build a new personalization product  from scratch. Thanks to my experiences, I am a firm believer in having dedicated and autonomous multi-functional teams to solve complex problems, especially when they involve learning.

As Data Scientists, we are often used to looking at problems from a data perspective, which has helped the teams I have worked with to gain huge amounts of domain knowledge. We strive also to make data-driven decisions, where running A/B tests or doing online and offline evaluations of the models we build are some of the most important tools we have. However, what does it look like to work in a multifunctional team?

In a Zalando team, we usually have one or two data scientists, one or two engineers, a product manager, a designer, and sometimes a business developer. The details change from team to team, but you get the picture. Not all of these people are dedicated to the team 100% of their time, sometimes a designer can work with two or three teams, depending on their areas of interest. The main advantage of working with this setup is that we are able to tackle uncertainties and risks from many more angles, and way faster than on a researchers-only team.

Something that I have learned when working with designers, is the many advantages of early testing and prototyping, and their customer-centric approach to problem-solving. Moreover, because they tend to work in different products from similar areas, the knowledge transfer usually happens more naturally and also faster, and completely changes the way we work. When working closely with our copywriting team, we learn how to communicate our products in the right way for our customers, and working with engineers we learn how to make sure to build machine learning solutions that scale and we are able to operate.

“For a week I moved away from coding and building models, and focused on deeply understanding the problem we wanted to solve with new tools and colleagues”

A very good example I have previously written about is the latest product I was in charge of building, where we were able to collaboratively design a prototype to solve our customer problem of “how can we make recommended content more transparent and relevant to our customers?” We did this in 4 days, writing only a minimum amount of code. We built 6 personalised prototypes for user testing, by manually adding “recommended” content into a static version of the Zalando App. Instead of using an algorithm, we “faked” the algorithmic result by using human expert curators to choose which content would be shown to each customer.

By faking the personalisation part, we were not only able to understand our customers’ expectations about our product, but we also saved months of development of an algorithmic solution that was not what the customer expected. In particular, the feedback we got from our customers was far more specific and natural than when using non-personalized prototypes. For example, instead of asking someone “imagine you love leather jackets and we recommend you matching boots”, we can know beforehand that they bought a leather jacket last week, and we created “recommendations” of the boots we thought would better match their style.

Working in this environment is also aligned with our principles of “radical agility” and autonomous teams. During the process, everyone involved gained customer understanding and domain knowledge from the problem we are trying to solve, something extremely valuable for Data Scientists. Moreover, iterating on this is way cheaper and faster than iterating on A/B test cycles, even when we have a really strong testing-as-a-service infrastructure.

This is only one example which shows how much I like working with people from different backgrounds and functions, which also proves how important diversity is for building great machine learning products, especially in a B2C market that operates on a European scale like Zalando does.

The DatSci Awards will be Celebrating Data Science Talent in a unique Awards Ceremony that is interactive & fun!  Anyone in the Data Science Community is welcome to join and it is a great opportunity for you to connect with some of the finest minds and organisations in the industry. Purchase your ticket here.

By | 2018-08-13T12:08:47+00:00 August 13th, 2018|

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