About this role
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 resultsDevelop, modify, and support in-house applications, working hand-in-hand with the data scientists and developers of the AC teamWork closely with clients during all stages of the application life cycleDesign the most adequate approaches to solve the identified problems within the company’s frameworkImplement 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 softwareAssist the operational teams with the deployment, monitoring and maintenance of the developed solutionsContribute to disseminate a culture of data driven solutions within CIBMaintain 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:
Fluent EnglishFrench and Portuguese are a plusEffective 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 fieldAlthough not strictly required a PhD on data-science, engineering or related fields is an advantage