Head of datascience

  • CDI 
  • Paris
  • Télétravail ponctuel autorisé
  • Bac +5 / Master
  • > 4 ans

La tribu



  • Organization / Management, Supply Chain

Le poste

Head of datascience

  • CDI 
  • Paris
  • Télétravail ponctuel autorisé
  • Bac +5 / Master
  • > 4 ans


At Wiremind, our goal since we started in 2014 is to create optimization systems for the transport, logistics, sports industries, without compromising on user experience. We build solutions that blend great design with cutting-edge technology (Deep Learning) to process vast quantities of data.

This leads us to work on many different kinds of projects: forecasting the demand for railway passengers between two major cities, handling millions of data points that we collect everyday through web-crawling, calculating the optimal way to fill an aircraft pallet with boxes of multiples dimensions and show the result in 3D, etc.
Our applications are used daily by hundreds of users among the largest players of each industry (railway companies, airlines, etc.) in many countries and several continents. We are now a team of 40+, and growing about +100% every 18 months.
Our business model is built on “software-as-a-service” solutions licensed through long-term contracts, allowing our strong growth to be based on strong profitability – without requiring any fundraising.

You will join the team in charge of developing CAYZN, our solution for Revenue Management that is used by major players of the railway and airlines industries. We recently achieved a very important milestone with this application by signing a contract with SNCF to handle the revenue management of all of their high speed trains, representing about 125 million passengers a year.

Job description


At Wiremind, the data-science team is responsible for the development, monitoring and evolution of all ML-powered forecasting and optimization algorithms in use in our Revenue Management systems through a library called Pythie. Our algorithms are divided in 2 parts:

  • a modelisation of the unconstrained demand using ML models (deep learning, LGBM, …) trained on historical data in the form of time-series
  • under-constraints optimizations solved using linear programming techniques

With the acceleration of our growth, the team is now entering a scaling phase where we will face the challenge to stay agile in terms of innovation while supporting and monitoring closely the in-production algorithms. To address this issue, we have organized the work around Kubeflow: a ML ops tool empowering data scientists to focus on high-added value tasks by maintaining a common framework and re-usable/ factorized components for all team members.

In this context, Wiremind is now looking for a head of ML capable of making necessary evolution on this existing framework and processes, developing and maintaining scalable ML pipelines and participating in integrating the pythie API into the real time architecture of the final application and growing the team as Wiremind expands.


In a team shaped to have all profiles necessary to constitute an autonomous departement (devops, software eng., data eng., IA, Operational research), you will be responsible for :

  • Leading the maintenance, development and research of new ML models for dataset that can span to billions of lines through reproducible, well documented and versioned pipelines
  • Shaping the evolution of our general ML training framework and its components (A/B testing, validation, feature extraction, training…)
  • Mentoring junior team member and onboard them in the framework
  • Ensuring code quality, factorization and documentation
  • Having a role in operations by developing and monitoring the link between Pythie and the final applications
  • Participating in business analysis in par with our customer success team and the final clients

Preferred experience


  • Python 3.7+
  • KubeFlow over an auto-scaled kubernetes cluster for orchestration
  • Druid as datastore
  • Redash for business analysis
  • Common ML librairies (tensorflow, lgbm, pandas, dash…)
  • Gitlab for continuous delivery


  • Strong computer-science background in python with an interest for code quality and good practices (unittesting, pep8, typing)
  • A first relevant experience in a data team with production ready pipelines using DAGs
  • Knowledge about at least one deep learning framework: tensorflow or pytorch
  • A pragmatic approach to ML where testing and frequent deliveries of incremental gains supported by validation / alerting processes to avoid regression is preferred to a long tunneled research process


  • A first experience with airflow or kubeflow
  • A first experience modelling time series

Recruitment process

The position is based in the city-center of Paris (métro Etienne Marcel) in our renovated office. We offer attractive pay packages (depending on your profile), closely linked to your performance and the company’s growth.

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