Since 2014 Wiremind has positioned itself as a technical company transforming the world of transport and events with a 360 approach combining UX software and AI.
Our expertise lies primarily in optimizing and distributing our clients capacity. We work on various projects such as ticket forecasting and pricing 3D optimization of air freight or sing competitor prices. Our applications are the preferred tool of companies such as SNCF United Airlines Qatar Airways or even PSG to visualize analyze and optimize their capacity.
Dynamic and ambitious we strive to maintain our technical DNA which is the engine of our success. The company profitable and selffinanced since its creation 10 years ago is mainly composed of engineers and experts and currently supports the growth of our business model based on softwareasaservice solutions.
Your missions
At Wiremind the Data Science teamis responsible for the development monitoring and evolution of all MLpowered 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 (e.g.deep learning boosted trees) trained on historical data in the form of timeseries
- Constrained optimizations problemssolved using linear programming techniques.
You will be joining a team shaped to have all profiles necessary to constitute an autonomous department (devops software and data engineering data science AIML operational research).
There under supervision of a Wiremind tutor and researchers from UBC () you will push the boundaries of stateoftheart causal inference modeling for time series.
As a research intern you will have the opportunity to contribute to innovative projects at the intersection of deep learning and causal modeling.
You will be involved in topics such as:
- Leveraging causal inference methods like Regression Discontinuity Design or Orthogonal Learning to analyze and model complex demand patterns using time series data.
- Developing stateoftheart deep learning architectures to improve the accuracy of current best models while maintaining causality and elasticity.
- Exploring the impact of pricing sequences on demand by modeling consumer behavior from a series of price changes instead of single adjustments.
Technical stack:
- Backend: Python 3.11 with SQLAlchemy
- Orchestration: Argo workflows over an autoscaled Kubernetes cluster
- Datastores: Druid and postgresql
- Common ML libraries/tools: TensorFlow/Keras LightGBM XGBooost Pandas Dask Dash Jupyter notebooks
- Model versioning and registry tool: Mlflow
- Gitlab / Kubernetes for CI/CD
- Prometheus/Grafana and Kibana for operations
Your profile
- Strong computer science background in python with a keen interest for code quality and best practices (unit testing pep8 typing)
- Knowledge about at least one major deep learning framework e.g. tensorflow pytorch
- A pragmatic prodoriented approach to ML: frequent incremental gains beat a grand quest for perfection.
What Would be a plus
- A first experience in a pricingrelated domain
- A wish to puruse a career in academia with a PHD following the internship
Our benefits
By joining us you will integrate:
- A selffinanced startup with a strong technical identity!
- Beautiful 700 m offices in the heart of Paris (Bd Poissonnire)
- Attractive remuneration
- A caring and stimulating team that encourages skills development through initiative and autonomy
- A learning environment with opportunities for evolution
You will also benefit from:
- 1 day of remote work per week
- A great company culture (monthly afterworks regular meetings on technology and products annual offsite seminars teambuilding
Our Recruitment Process
- An initial discussion with our Talent Acquisition Manager
- A technical test to be prepared
- A last interview at our offices to discuss your technical test with the Hiring Manager and meet with members of the team
Wiremind is committed to equality of opportunity diversity and fairness. We encourage all candidates with the necessary experience to apply for our job offers.