Open position at ShipMonk Research & Development
- Work schedule
- Rohanské nábř. 29, 186 00 Praha 8-Karlín, Česko
Hi, we are ShipMonk and we provide logistics services in the field of order fulfillment. Our main business is in the USA, but Prague is the center of the development of our modern platform. We are looking for someone who has a few years of experience under their belt and will enjoy taking our systems to the next level.
We are looking for a data scientist who likes to program in Python. You know how to think things through, like to get to the heart of the business, jump into implementing the actual solution with vigor, and use data to show how we've improved.
You will develop a platform that covers smart and computationally intensive solutions, help design and optimize warehouse processes, simulate and analyze new ideas and discuss them within the team.
- Up to 100k orders per day in high season
- Over 1,000 clients, which are small and medium sized e-shops mostly from the US
- Our platform is the solution for the entire process, from stocking the goods to shipping the ready packages to all corners of the world
What should you know/have?
- Experience in developing (production) solutions in Python
- Retrieve necessary data from SQL and other database and data sources
- Design, evaluate, compare and implement algorithms and data structures
- Good communication skills and business point of view
- Be a team player
- Analyze and demonstrate a problem by data exploration and visualization
- Optimization algorithms and artificial intelligence
- Object-oriented programming
What will you be doing with us?
- Develop a platform for smart and computationally intensive solutions.
- Assist with the design and optimization of warehouse processes.
- Simulate and analyze new ideas to move us forward.
- Evaluate current solutions and identify missing data.
- Collaborate and brainstorm within the Research and Development team, as well as our business teams.
- Refine our Data Science platform and approach to solving complex problems.
What have we accomplished so far in the Data Science team and how?
- Reducing the number of boxes used from more than 20 to less than 10 with only a small degradation in shipping rates (used Master Theorem for the computationally intensive part and the hungry algorithm for optimization)
- Predicting product sales using Machine Learning models
- Distribution of products into different warehouse zones (application of graph theory knowledge)
- Optimization of box selection for stacking (custom modification of the backpack problem)
- Optimization of forklift paths and other transits so they do not block each other
- Balancing of workload incoming to various processes to minimize the dead time
- Deriving weights of specific products from available weights of entire orders
- Balancing the automatic conveyor
If you like our story, we look forward to hearing your resume, profile, story, whatever. There are no limits to creativity. Our Recruiter, Veronika, will get back to you as soon as possible. We look forward to hearing from you.