Job description:
Develop End to End Product Lifecycle:
- Develop Machine Learning (ML) and Artificial Intelligence (AI) solutions i.e., collect and label data, setup and train models, test and evaluate results
- Develop, modify, and support in-house applications, working hand-in-hand with the data scientists and developers of the AC team
- Work closely with clients during all stages of the application life cycle
- Design the most adequate approaches to solve the identified problems within the company’s framework
Implement and assist the deployment and maintenance of models and software:
- Follow industry best practices to implement and scale out the developed solutions into production grade software
- Assist the operational teams with the deployment, monitoring and maintenance of the developed solutions
- Contribute to disseminate a culture of data driven solutions within CIB
- Maintain a strong market watch on the latest developments on methodologies, technologies, and tools"
Main Tasks:
- Develop End to End Product Lifecycle
- Implement and assist the deployment and maintenance of models and software
Technical Skills:
- Ability to explain the results of a model and the different elements impacting it
- Statistical and quantitative analysis skills (e.g., machine learning models, statistical models, Bayesian statistics, probability theory, time series analysis,
- Knowledge of at least one ML development stack (e.g. scikit-learn, keras, …)
- Knowledge of NLP techniques
Language Skills:
- French and Portuguese are a plus
- Effective Programming skills (e.g., Python, R, Java, Go) and algorithmic knowledge.
- Knowledge of databases and associated tools (SQL and NoSQL)
- Familiarity with continuous integration tools (pref. Jenkins, Git)
- Knowledge of deep learning architectures and frameworks (e.g., PyTorch, TensorFlow,…)
Soft Skills:
- Ability to think abstractly and creatively
- Excellent communication and interpersonal skills
- Flexibility: ability to work in a changing environment
Education
- Engineer School/master’s degree from university with a specialization in mathematics, statistics, computer science, data science or any engineering related field
- Although not strictly required a PhD on data-science, engineering or related fields is an advantage
