CONTEXT

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. Our algorithms are divided in 2 parts:

• a modelling of the unconstrained demand using ML models (deep learning, LGBM, …) trained on historical data in the form of time-series
• constrained optimizations problems 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 are starting to organize the work around Kubeflow (https://www.kubeflow.org/): 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 data scientist capable of working with a complex codebase in order to make necessary evolution on this existing framework and processes while developing and maintaining scalable ML pipelines.

WHAT YOU WILL DO
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 :

• Participating to the maintenance, development and research of new ML models through reproducible, well documented and versioned pipelines
• Improving Machine Learning research at Wiremind (e.g. by taking into account new variables or modelling inherent constraints)
• Developing monitoring tools to diagnose and improve the existing ML models (using mainly Dash and plotly)

TECHNICAL STACK

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

WHAT IS IMPORTANT TO US

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

WHAT WILL BE A PLUS

• A good knowledge of tensorflow
• A first experience modelling time series