Data Scientist Operations Research – Team Stock Allocation (Capacity Steering)

ol is changing retail to make daily life simpler for 13.5 million Dutch and Belgian customers. We are joined on this mission by more than 50,000 partner sellers that do business on our retail tech platform.

How do you make our customers happy?

Bol is changing retail to make daily life simpler for 13.5 million Dutch and Belgian customers. We are joined on this mission by more than 50,000 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.

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 five gigantic warehouses. Because of the enormous complexity, 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 way. This ranges from solving the shortest path problems in the warehouses, to real-time load-balancing the orders across the warehouses to avoid bottlenecks.

In this role, you make an impactful contribution to that goal by developing clever algorithms that determine which products should be stored at each warehouse to ensure cost-effective processing and maximize capacity utilization during peak demand. Your algorithms will ensure products are always available to our customers and will be sent to them fast, cheap and in a sustainable way.

The biggest challenge 

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 our logistics landscape, designing and implementing smart algorithms is the next logical 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? These are questions where our Data Scientists dive into.

What you’ll do as Scientist Operations Research

In this position you join a forward-thinking, multi-disciplinary team of data scientists, software engineers, and business colleagues (product manager, product analyst, and logistical process engineers). You help them develop an in-depth understanding of complex logistical processes, and spot opportunities for optimizations. This includes identifying (potential) bottlenecks and daily check-ins with stakeholders. You translate your findings into data-driven optimization and simulation models and continuously adjust and improve the models as new challenges arise.

Why you can make a difference

The challenges we face are both complex and, when solved, very rewarding. Joining our team now, right after a major overhaul of our IT stack, poses some interesting challenges. 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. By virtue of your pragmatic approach and by leveraging the deep knowledge of our business, you manage to strike the right balance between battling complexity and achieving working results. You’ll get the chance to 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.

Other responsibilities include:

• 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 balance between ‘battling complexity’ and achieving concrete results
• Team up with software 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

3 reasons why this is (not) for you

Pros

• You have at least 5 years of working experience in the data science domain, mainly focused on OR
• You have solid programming skills in Python, Streamlit and Airflow
• You have experience in working with BigQuery, SQL, AWS, Google Cloud Platform or similar cloud technologies

Cons

• Discomfort with complexity and ambiguity
• No interest in stakeholder engagement
• Lack of interest in applied problem-solving

This is where you’ll work  

Team Stock Allocation consists of several product colleagues, software engineers and data scientists that together work hard to realize the stock distribution that best balances our three key drivers: minimizing cost, maximizing capacity utilization and customer proposition. This team is part of a larger domain called Capacity Steering. This domain, which includes a total of three product teams and 40 colleagues, is responsible for making the optimal usage of our total logistical network, so that our logistical performance matches our customer demand.