Coolblue. Once, it was only a college start-up. Today, it`s an online enterprise that`s made up of 8 physical stores and over 325 specialized webshops. Ever since we began in 1999, we`ve had but one goal: to make customers happy. We provide expert advice and we’re obsessed with customer satisfaction. Together, we work our butts off in order to amaze our customers.
As an Advanced Data Analyst / Data Scientist you use the data of millions of visitors to help Coolblue act smarter.
+ You’re going to be working as a true Data Scientist. One who understands why you get the results that you do and apply this information to other experiments
+ You’re able to use the right tools for every job
+ Your job starts with a problem and ends with you monitoring your own solution
- You have to crawl underneath the foosball table when you lose a game
Your challenge in this sprint is improving the weekly sales forecasting models for the Christmas period. Your cross-validation strategy is ready, but before you can begin, you have to query the data from our systems and process them in a way that allows you to view the situation with clarity.
First, you have a meeting with Matthias, who’s worked on this problem before. During your meeting, you conclude that Christmas has a non-linear effect on sales. That’s why you decide to experiment with a multiplicative XGBoost in addition to your Regularised-Regression model. You make a grid with various features and parameters for both models and analyze the effects of both approaches. You notice your Regression is overfitting, which means XGBoost isn’t performing and the forecast isn’t high enough, so you increase the regularization and appoint the Christmas features to XGBoost alone.
Nice! You improved the precision of the Christmas forecast with an average of 2%. This will only yield results once the algorithm has been implemented, so you start thinking about how you want to implement this.
'I believe I'm working in a great team of enthusiastic and smart people, with a good mix of juniors and seniors. The projects that we work on are very interesting and diverse, think of marketing, pricing and recommender systems. For each project we try to use the latest research and machine learning techniques in order to create the best solutions. I like that we are involved in the projects start to end, from researching the problem to experimenting, to putting it in production, and to creating the monitoring dashboards and delivering the outputs on a daily basis to our stakeholders. The work environment is open, relaxed and especially fun' - Cheryl Zandvliet, Data Scientist
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