About The Role
As a Machine Learning Engineer at Alma, you will be an integral part of the AI/ML team within the Risk Modelling department. Your primary responsibility will be to develop and deploy machine learning models that are crucial for real-time credit risk evaluation, a core component of Alma's mission to provide a frictionless payment experience while minimizing defaults and maximizing client acceptance.
Responsibilities
- Own the entire Machine Learning lifecycle, from data collection to model development, deployment in production, and monitoring.
- Utilize ML algorithms and statistical methods to iteratively improve the performance of our credit scoring models.
- Collaborate closely with multiple teams to ensure successful execution of projects.
- Understand Alma's business model and proactively identify new opportunities to leverage AI.
- Stay updated with ML research, learn new techniques, and apply them to drive business impact.
- Share knowledge and expertise with teammates and contribute to the collective learning environment.
Qualifications
- Minimum confirmed full-time permanent experience in machine learning with a master's degree in a relevant field (computer science, machine learning, mathematics, engineering, etc.).
- Solid understanding of ML fundamentals, including algorithms and concepts such as data leakage, bias-variance tradeoff, under/over-fitting, etc.
- Production-level development experience in Python and SQL.
- Proficiency with cloud computing platforms, preferably GCP.
- Advanced analytical skills, capable of deep-diving into problems and delivering pragmatic, tangible results.
- Autonomy and ownership on complex projects, capable of navigating uncertainty while staying focused and prioritizing effectively.
- Energetic and enthusiastic team player with excellent communication skills.
- Full professional proficiency in both French and English.
Preferred Skills
- Experience with container-based deployments (Docker, etc.).
- Experience interacting with graph databases.
- Experience in credit scoring or the BNPL ("Buy Now Pay Later") industry.
- Comprehensive knowledge of the entire ML application lifecycle, from research to production use.
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