Ml Engineer Intern

Since 2014, Wiremind has positioned itself as a **tech company** transforming the world of transport and events with a 360° approach combining **UX, software, and AI**. 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. **Your missions**: 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 The team is scaling and needs to stay agile while supporting and monitoring deployed algorithms. We use Argo and MLFlow to help data scientists focus on high-value tasks with a common framework and modular components. As we grow, we manage more data sources and predictive models. To maintain high prediction accuracy and data quality, we seek an analytically-minded Data Scientist Intern with a strong background in statistics. 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, you will leverage state-of-the-art AI/ML methods and ironclad validation processes to deliver robust, interpretable prediction systems. As a ML Engineer intern, you will work on the following topics: - Designing/training of new models (neural networks or decision trees) to improve our current models in production - Tackling railway and cargo demand specific challenges: imbalanced data, skewed data distributions, features engineering - Performing in-depth model evaluation: to properly assess model performance and comparison with other models. - Taking part in the improvement of our training pipeline framework (tests, automation..) - Our technical stack _ - Python 3.10+ - Argo workflows over an auto-scaled Kubernetes cluster for orchestration - Druid and postgresql as datastore - Common ML libraries/tool: TensorFlow/Keras, LightGBM, XGBooost, Pandas, Dask, Dash, Jupyter notebooks - Mlflow as model versioning and registry tool - Gitlab for continuous delivery/integration **What we're looking for**: - Current studies (at least Master II) related to the following fields: data science, computer science, applied mathematics. - **You are looking for an end-of-study internship (starting around March 2025 for 6 month)**: - Good understanding of basic machine learning tasks (classification, regression) and mathematical concepts behind it - Good understanding of basic evaluation metrics : accuracy, recall, precision, RMSE,.... - Hands-on experience with python language and standard data tools: Pandas, NumPy - Strong curiosity and desire to learn - Strong computer science background in Python, - A good experience in model development (Kaggle, class or personal projects,..) **What we offer for 6 months**: By joining us, you will integrate: - A self-financed startup with a strong technical DNA - Beautiful 700 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 concrete opportunities for evolution ‍ You will also benefit from: - Training on demand - A hybrid policy: a day of remote work per week and the possibility to work occasionally from abroad - A great company culture (monthly afterworks, regular meetings on technology and products, annual off-site seminars, team-building ) **Our recruitment process**: - An initial discussion with Anne-Laure, our Talent Acquisition Manager - A screening with the manager - A technical test to prepare at home - A technical interview at our offices with the manager