ML Researcher

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 marketing our clients' capacity. We work on various projects such as ticket forecasting and pricing, 3D optimization of air freight or scraping 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 self-financed since its creation 10 years ago, is mainly composed of engineers and experts and currently supports the growth of our business model based on "software-as-a-service" solutions.

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 learning, boosted trees) trained on historical data in the form of time-series
• Constrained optimizations problems solved 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 state-of-the-art 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 state-of-the-art 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.

• Backend: Python 3.11+ with SQLAlchemy
• Orchestration: Argo workflows over an auto-scaled 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

• 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, prod-oriented approach to ML: frequent, incremental gains beat a grand quest for perfection.

• A first experience in a pricing-related domain
• A wish to puruse a career in academia with a PHD following the internship

By joining us, you will integrate:

• A self-financed startup with a strong technical identity
• Beautiful 900 m² offices in the heart of Paris (Bd Poissonnière)
• 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 off-site seminars, team-building…)

• A screening interview with Yasmine, our Talent Acquisition Specialist
• A technical test to be prepared
• A last interview at our offices to discuss your technical test with Yacine, our ML team lead 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.