Data Scientist - Internship

Company Description

Founded in 2014, Shippeo is a global leader in real-time multimodal transportation visibility, helping major shippers and logistics service providers operate more collaborative, automated, sustainable, profitable, and customer-centric supply chains.

Hundreds of customers, including global brands such as Coca-Cola HBC, Carrefour, Renault Group, Schneider Electric, Total, Faurecia, Saint-Gobain and Eckes Granini, trust Shippeo to track more than 28 million shipments per year across 92 countries.

Having already raised €110 million in funding, Shippeo grows on average by 80% year on year. Our team of Shippians comprises 28 different nationalities, speaking a total of 24 languages.

Job Description

We are looking for an Intern in Data Science to join our Data & AI/ML tribe.

The Data & AI/ML tribe is responsible for leveraging the large amount of data that Shippeo has been acquiring over the course of running the platform and rolling it out to multiple shippers and carriers, to get insights from it.

One of the main features the team builds and improves is Shippeo’s proprietary Machine algorithm that predicts Estimated Times of Arrival (ETA) of vessels, which is an extremely difficult exercise due to all the uncertainties in ocean transportation (weather conditions, port congestion, time spent on ports to load and discharge merchandises, ...). 

We are constantly looking for new ways to make the ETA prediction as accurate and reliable as possible, to help our users anticipate delays.

In the Shippeo platform, Ocean ETA is mainly used to answer the following customer needs:

1. When exactly will a vessel arrive at a given port and when will this vessel be able to discharge its containers?

2. Is there a risk of delay at any of these milestones?

In this context, we would like to focus on port congestion problematic and better understand: : 

• What can cause this congestion : Can we predict it using vessel operations inside the ports ? Is there a form of seasonality? … 

• What are its effects at vessel level : Are all vessels affected by port congestion in the same way ? In the case of a congested port, are the first in the first out? 

This internship will consist in exploring and proposing different strategies to improve our ML models performance. This will for instance involve : 

• Training of a classification model to predict whether a vessel will be anchored at its arrival at the destination port using data related to port congestion 

• Training of a regression model to predict the duration of this anchorage in the event that it occurs 

• Dashboards to visualize ports congestion at the moment and also what we predict will happen

Qualifications

• You are pursuing a MSc degree (or equivalent) with a major in Data Science, and are in your final year 

• Knowledge and experience with relational databases (SQL, data modeling)

• Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy

• Programming skills in Python and experience with scientific programming libraries (Pandas, Numpy, Scipy)

Additional Information

Recruitment Process : 

1. Preliminary call

Technical interview and final interview
 

Our values

We are looking for talents who share our values:

•  🚀 Ambition
• 💙  Care
• 🎯  Deliver
• 🤝  Collaboration

Find out more about our values in Our Culture Book

If you identify with our values and enjoy working in a fast-paced and international environment, Shippeo is just the place for you!

Would you like to discover more? Click here :

• Website
• Linkedin
• Instagram
• Shippeo Tech blog (Medium)
• Shippeo blog
• Twitter
• Facebook
• Youtube