Bol. is changing retail to make daily life simpler for 13 million Dutch and Belgian customers. We are joined on this mission by 49,000 partner sellers that do business on our retail tech platform. This represents a combined assortment of approximately 41 million articles and peak sales of 1,800 orders per minute.
In order to process these sales, our logistics operation has grown tremendously over the past years, currently shipping roughly 500,000 items per day from one of our 5 gigantic warehouses. Because of the enormous complexity of the bol. logistics landscape, data science and operations research are playing a crucial role in ensuring that we make optimal use of our resources and deliver every parcel on time in a cost-effective manner. This ranges from solving shortest path problems in the warehouses to ensure efficient order picking under tight time constraints, to real-time load-balancing the orders across the warehouses to avoid bottlenecks and formulating and solving massive integer programming problems that determine where we place our stock.
In this role, you make an impactful contribution to that goal by developing clever algorithms that enable us to deploy logistical resources within our warehouses rationally. Making sure that every item is processed efficiently and leaves the warehouse on time.
We need to safeguard that our retail organization and sales partners deliver on our ambitious logistics promises. Given our enormous growth over the past years in order volume and the increasing complexity of the logistics landscape – e.g., we offer logistics as a service – designing and implementing smart algorithms is the logical next step. How can we fully utilize logistical resources cost-efficiently? How can we ensure stock is where it should be? And how can we fulfill wildly varying customer orders in the best possible way?
In this position you join a forward-thinking, multi-disciplinary team of data scientists, software engineers and business analysts and help them develop an in-depth understanding of intricate logistical processes. This includes identifying (potential) bottlenecks. Translate your findings into data-driven optimization and simulation models. And continuously adjust and improve the models as new challenges arise.
The fluid nature of bol. – change is the only constant – and the extremely data-rich environment make for an interesting playing field. The challenges we face are both incredibly complex and, when solved, vastly rewarding. You mainly work on scalable, long-term solutions, but when (potential) issues arise you’ll need to think on your feet to quickly get to the core problems and make the appropriate decisions. Often going on limited and/or unvalidated information. By virtue of your pragmatic approach and by leveraging the deep knowledge of our algorithms, you manage to strike the right balance between battling complexity and achieving working results. You’ll build solutions from the ground up with the software engineers in your team and assume full ownership of your solutions. Keeping these solutions (and yourself) responsive during peak hours is key.
Leverage logistical insights to develop optimization models, simulation models and smart heuristics for the processes across our logistics
Tweak and improve those models on a challenge-by-challenge basis
Strike the right balance between ‘battling complexity’ and achieving concrete results
Team up with engineers to build and implement new or improved solutions
Check in regularly with your colleagues from operations for feedback on your solutions and to identify problems and potential improvements
Ensure solutions are robust and responsive, even/especially during peak hours
To be successful as Senior Data Scientist Operations Research, you’ll need solid experience in optimization and simulation, as well as programming skills. Because you’ll work closely with colleagues in different domains and with a wide variety of skill sets and backgrounds, you also need to be open to approaching issues from different perspectives. This way of working often yields the clearest insights and the best results. And as our environment is constantly changing, we expect our data scientists to be eager to learn, both by honing their existing skills and by venturing into new territory. As the senior data scientist in your team you’ll also coach other data scientists to further their development, and you support the product manager in developing a long-term vision.