Machine Learning Engineer Intern

at ING

📍 Milan, Italy

EUR 18,000 per year

INTERN

✅ Hybrid

✅ Visa Sponsorship

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

Algorithms Azure CI/CD @ 3 Communication @ 6 DevOps @ 3 Git @ 3 GitHub Machine Learning @ 2 Python @ 3 scikit-learn @ 2

Details

The team applies advanced analytics, including machine learning, quantitative algorithms, and data mining techniques, to obtain business insights and solve problems. The work spans the entire lifecycle, from initial design to final implementation, with stakeholders across business, risk, retail, and corporate banking.

The employee will focus on developing an interactive dashboard for computing the profitability and risk assigned to a finance product. Students interested in writing their thesis on this topic are encouraged to apply.

Responsibilities

Build and maintain a robust codebase supporting the creation of machine learning models.
Train, deploy, and monitor machine learning models tailored to various business needs.
Conduct ad hoc data analyses.
Develop dashboards and tools to improve data accessibility and support a more data-driven organization.
Develop an interactive dashboard for computing the profitability and risk assigned to a finance product.

Requirements

At least 2 years of practical experience with a programming language such as Python.
Knowledge of object-oriented programming and Git workflows.
Experience with CI/CD pipelines and DevOps practices.
Solid data-handling skills.
Fluent written and spoken English.
Strong verbal and written communication skills.
Self-driven, with the ability to find solutions independently.
Ability to work collaboratively in a team environment.
Energetic, self-motivated work ethic.
Ability to communicate effectively with people at multiple levels.
Ability to perform under pressure.
High ethical standards, honesty, and integrity.

Nice to Have

Personal public projects on GitHub, Azure, or similar platforms.
Familiarity with machine learning libraries and frameworks such as scikit-learn.
Experience with credit decision models, business models such as churn or propensity models, or clustering techniques.