Senior Data Scientist / ML Engineer (H/F)

  • Paris, France
  • Employees can work remotely
  • Full-time
  • Department: Product &Tech

Company Description

Founded in 2014, Shippeo is a French SaaS company leading the European market in helping shippers and logistics companies track their freight shipments in real-time to improve visibility throughout their end-to-end supply chains. ūüöö¬†

Having already raised ‚ā¨71 million in funding, Shippeo is growing rapidly. The team has more than doubled in size to 160 within 2020 and the scaling will continue throughout 2021. Our team of Shippians comprises 27 different nationalities, speaking a total of 29 languages !

Job Description

What Shippeo does

Our product is composed of a mission critical SaaS web platform, with high traffic inbound/outbound integrations. Our mission is to anticipate problems and proactively alert end-customers so they can efficiently manage exceptions. We achieve this by collecting and matching millions of theoretical and real data from different stakeholders.

Shippeo gives visibility to shippers, carriers and customers by answering the following questions:

  • where is the truck, and are there any foreseeable delays?

  • what has actually been loaded and/or delivered, and are there any discrepancies?

  • what are the levers for improvement for the transport operations?

 The technical team is structured in three Tribes, each split into multiple feature teams:

1. Connect & Go Live: quickly connect shippers & carriers to have the project ‚Äėlive‚Äô

2. Track to React: make our product as sticky as possible for daily users

3. Analyze & Predict: leverage our data to build data products (extraction, analytics, decision support tools, algorithms)

 

What you will do

We are looking for a Senior Data Scientist / ML Engineer to join our Analyze & Predict tribe. 

The Analyze & Predict 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 products the team builds and improves is Shippeo’s proprietary Machine Learning algorithm that predicts Estimated Times of Arrival (ETA) of trucks, which is an extremely difficult exercise due to all the uncertainties in road transportation (traffic, weather conditions, driving regulations, time spent on on loading or delivery site, etc.). We are constantly looking for new ways to make the ETA prediction as accurate and reliable as possible.  

Your missions will include: 

  • Build and maintain data pipelines that will feed our ML models¬†

  • Experimenting and prototyping new approaches of ETA predictions

  • Reviews technical literature and challenges existing models and algorithms

  • Designs complex algorithms to solve hard business problems

  • Promote and present data science work both within and outside of the organisation

  • Mentors data analyst and data scientists

Qualifications

Required:

  • 4-5 years of experience in building machine learning systems to solve business problems

  • Experience building, maintaining, testing and optimizing ML pipelines and architectures

  • Programming skills in Python and mastering of scientific programming libraries¬†

  • Advanced working knowledge of SQL, experience working with relational databases and familiarity with a variety of databases

  • Strong algorithmic skills and knowledge of popular data structures

  • Good understanding of deep learning concepts and experience with one deep learning framework (Tensorflow, Pytorch, etc.)

  • Strong skills in troubleshooting and support abilities.

  • Familiar with cloud environments¬†

  • Used to work in an Agile environment and to write production ready code.

 Desired: 

  • Knowledge of Google Cloud Platform

  • Experience with building containerized applications¬†

  • Knowledge of compiled language (Golang, Scala, Java)

Additional Information

Interview process

  1. Preliminary call

  2. Phone interview with the hiring manager

  3. Case study preparation 

  4. Case study presentation and interview with the rest of the team

  5. Final interview with a co-founder

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