About the job

Our global mission is to enable Alma to grow quickly and securely by anticipating and preventing any risks that could endanger the company. In a nutshell, this means:

• Building tools and processes that allow us to accurately assess the risk level of our merchants during onboarding and throughout the entire relationship
• Continuously improving our customer scoring model with the objective of offering a frictionless payment experience while minimising defaults and maximising client acceptance
• Preventing and thwarting any fraud attempts from our clients or merchants

About the mission

As a Machine Learning Engineer, you will join the AI/ML team (part of the Risk Modelling team). You will be responsible for developing and deploying ML models used in our real-time credit risk evaluation, which are at the core of Alma's mission to offer a frictionless payment experience while minimising defaults and maximising client acceptance.

Joining forces with the team, you will:

• Own the entire Machine Learning lifecycle, from data collection to model development, deployment in production and follow-up of the monitoring pipeline of our ML products
• Leverage ML algorithms and statistical methods to iteratively improve the performance of our credit scoring and extend its capabilities
• Collaborate closely with multiple teams to ensure the successful execution of your projects
• Understand Alma's business model and proactively identify new opportunities to leverage AI within other teams
• Follow ML research, learn new techniques, and apply them to drive business impact
• Share your knowledge and expertise with your teammates and Almakers

Over the course of your experiments and deployments, your impact on customer experience and our bottom line will be significant and easily observable.

About you

What would make you a good fit for the role:

• Minimum a confirmed full-time permanent experience (internships & apprenticeships excluded) 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: unit testing, versioning (git), and documenting your code
• Proficiency with cloud computing platforms (preferably GCP)
• Advanced analytical skills, with the ability to deep-dive into a problem and quickly deliver pragmatic, tangible results, without getting lost in unnecessarily complex reasoning
• Autonomy and ownership on complex projects, capable of getting out of your comfort zone while staying focused, prioritising and communicating in a context of uncertainty
• Energetic and enthusiastic team player with very good communication skills
• Mandatory full professional proficiency in both French and English

What would make you stand out of the crowd:

• Hands-on experience with container-based deployments (Docker, etc.)
• Experience interacting with graph databases
• Experience in credit scoring or the BNPL ("Buy Now Pay Later") industry
• Overall knowledge of the entire ML application lifecycle, from research to production use

ML Stack: Python, FastAPI, VertexAI, BigQuery, PostgreSQL, Github, scikit-learn

About the recruitment process

• Phone interview with Recruiter (30 mins)
• Technical interview with Senior MLE (30 mins)
• Applied ML / Python interview (60 mins)
• ML system design interview (60 mins)
• Final interview with Head of Risk Modelling (60 mins)

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