📍 Paris | Fluent 🇫🇷 & 🇬🇧
E-commerce is booming — but independent brands still struggle to match the seamless logistics of giants like Amazon. At Bigblue, we’re on a mission to change that.
We help brands by democratizing best-in-class operations: scaling faster while delivering a five-star customer experience.
Since 2018, we’ve built a tech-driven logistics platform powering 500+ brands — from fast-growing DTC players to global names like MUJI, Aigle, and Cabaïa. With 110+ employees across Paris, Madrid, and London, our proprietary WMS (Warehouse Management System) Atlas, and a network of 7 warehouses across Europe, we’re solving logistics challenges that others haven’t dared to tackle — making fulfillment faster, smarter, and greener.
Backed by $20+M in funding and trusted by the next generation of e-commerce leaders, we’re scaling rapidly — from 10 million orders in 2024 toward ambitious enterprise expansion. The journey ahead is bold, and we’re looking for the right partner to help us lead the way.
📊What data means for Bigblue
At Bigblue, our warehouses generate the data that drives every decision. Every item moved, picked, or shipped creates real-time insights into how our operations work and where improvements can be made. As our network grows across Europe, these data challenges become even more exciting: optimizing storage, shorten operator routes, and improving overall warehouse productivity.
🚀 Missions
As a Data Science Intern, you’ll help us make warehouses smarter and more efficient by developing optimization algorithms that directly impact daily operations.
Your main focus will be the order batching problem — deciding how to group customer orders so operators can pick items faster while minimizing walking distance. You’ll design and test intelligent batching algorithms that must handle operational constraints, like order characteristics (weight, volume) and priorities (express vs standard), all while running in real time at scale.
During your internship, you will:
➡️ Explore and benchmark optimization methods
➡️ Build and test algorithmic solutions using real warehouse data
➡️ Evaluate their impact on key metrics such as picking time and walking distance
By transforming complex research problems into practical solutions, you’ll play a key role in boosting warehouse productivity — enabling faster deliveries, lower costs, and smoother operations for hundreds of brands.
