Machine Learning Engineer Intern

Description

The team applies advanced analytics—including machine learning, quantitative algorithms, and data mining techniques— to obtain business insights and solve problems. We are actively involved throughout the entire lifecycle, from initial design to final implementation. Our stakeholders span all areas of the bank, including business, risk, retail, and corporate banking.

Our scope of work includes:

Building and maintaining a robust codebase that supports the creation of ML models

Training, deploying, and monitoring machine learning models tailored to various business needs.

Conducting ad hoc data analyses

Developing dashboards and tools to enhance data accessibility and support a more data-driven organization.

The employee will focus on developing an interactive dashboard for computing the profitability and risk assigned of a finance product.

For this purpose, we are looking for students interested in writing their thesis on this topic.

Experiences:

Practical experience (at least 2+ years) with programming language such as Python

Knowledge of Object-Oriented Programming and Git workflows

Experience with CI/CD pipelines and DevOps practices

Requirements

• Demonstrated solid data handling skills.
• Fluent written and spoken English
• Strong verbal and written communication skills
• Personal public projects on GitHub/Azure or similar
• Familiarity with machine learning libraries and frameworks (e.g. scikit-learn...)
• Experience in at least one of the following:
• Credit Decision Models
• Business Models (e.g., Churn, Propensity)
• Clustering techniques
• Personal Characteristics:
• Self-driven with the ability to figure out solutions by him or herself
• Ability to work collaboratively in a team environment.
• Must possess an energetic, self-motivated work ethic.
• Effectively communicate with many levels.
• Proven to perform under pressure.
• High ethical standards, honesty, integrity.