For internships, we are looking at duration of 6 months and we are flexible on the starting date (the earlier the better!). The intern will participate to the life of the LAB and will take ownership of one or more topic. We have a great variety of topics, and some of the historical propositions included:
· Prediction of which products are the most likely to be interesting for a given client.
· Automated Generation of Market Comment.
· Optimal Risk Management of Interest Rates Swap Risk.
· Regime disentanglement for financial mixture of experts models.
· Generative modelling for model control.
· Transformers for quantitative investment strategies.
Based on the skillset & business need, we can select a valuable proposition for you!
Responsibilities
Direct Responsibilities
1. Explore and examine data from multiple diverse data sources.
2. Conceptual modeling, statistical analysis, predictive modeling and optimization design.
3. Data cleanup, normalization and transformation.
4. Hypothesis testing: being able to develop hypothesis and test with careful experiments.
Contributing Responsibilities
1. Help build workflows for extraction, transformation and loading of different data from a variety of sources and enable linking them to existing systems and datasets.
2. Ensure the integrity and security of data.
Technical & Behavioral Competencies
1. Education in data science, who not only have experience in solving complex problems but as well understand how and why the model work the way they do.
2. Knowledge of key concepts in Statistics and Mathematics such as Probability Theory, Inference, and Linear Algebra.
3. Knowledge or experience in Machine Learning procedures and tasks such as Classification, Prediction, and Clustering.
4. Programming skills in Python and knowledge of common numerical and machine-learning packages (NumPy, scikit-learn, pandas, Keras, TensorFlow, PyTorch, langchain).
5. Ability to write clear and concise code in python.
6. Intellectually curious and willing to learn challenging concepts daily.
7. Involvement with the Data Science community through platforms such as Kaggle, Numerai, Open ML, or others.
8. Knowledge of current Machine Learning/Artificial Intelligence literature.