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 (e.g. deep 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 departement (devops, software and data engineering, data science, AIML, operational research). 

There, under supervision of a wiremind tutor and researchers from the LPSM (https://www.lpsm.paris/) you will research a new avenue of modelisation. 

In this project, we propose to introduce new deep learning architectures inspired for instance from [2] to propose parametric time series generative modelling for the unconstrained demand with price elasticity. These approaches are based on general state space models where the observations are random functions of the latent states. In such a setting, inference procedures require the posterior distribution of the latent states given the observations that are intractable. Traditional Markov Chain Monte Carlo and Sequential Monte Carlo methods are too computationally intensive to be used in the context of this project so that we propose to explore variational inference and VAE methods. Such approaches would allow to introduce approximate posterior distributions and predictive distributions for the unconstrained demand with price elasticity. These distributions can then be used to provide uncertainty quantification metrics. The main objective of this project is to propose new models that specifically takes into account decreasing monotone constraint between demand and price. This is an open problem both from practical and theoretical perspectives. One solution is to introduce specific conditional likelihood to allow dynamic pricing with monotonicity constraint. We will also investigate the introduction of constraints in the reconstruction loss of the variational model to satisfy the required monotonicity.

Technical stack:

• Python 3.11+

• Argo over an auto-scaled kubernetes cluster for orchestration

• Druid as datastore

• Common ML libraries: tensorflow, lgbm, xgboost, pandas, dask, dash, mlflow

• Gitlab for continuous delivery

• 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.

WHAT WOULD BE A PLUS

• A first experience in a pricing-related domain

• A wish to puruse a career in academia with a PHD following the internship

• A first, short introduction call with our Talent Acquisition Manager and/or a member of the Tech Team

• A short technical test to be prepared before next interview will be sent ; you will be invited at our Paris offices for the case study restitution, and meet the team

Wiremind is committed to equal opportunities, diversity and fairness. We encourage all candidates with the necessary experience to apply for our vacancies.