Alma shapes the Fintech landscape. We strive to serve and empower consumers and merchants by developing innovative solutions that redefine their purchase experience.

About the role

We're seeking a Machine Learning Engineer (MLE) to join our expanding AI/ML team within the Data department. As we broaden our Machine Learning (ML) capabilities across the organisation, we're looking for an enthusiastic individual to contribute to our core risk assessment models and help drive innovation in new areas of the business.

As a Machine Learning Engineer at Alma, you will join a team that has successfully developed critical ML solutions for our core business, including:

• Customer scoring models that balance frictionless payment experiences with default minimisation and client acceptance maximisation
• Risk assessment tools for merchant onboarding and ongoing evaluation
• Fraud prevention systems for both clients and merchants

While these risk-related topics remain our priority, we are now expanding our ML capabilities to other exciting areas of the business. In this role, you will also:

• Contribute to the maintenance and improvement of our core risk assessment and fraud prevention models
• Collaborate on new ML initiatives across various business units, applying your skills to diverse challenges beyond risk management
• Participate in the full ML lifecycle, from data collection and preprocessing to model development, deployment, and monitoring
• Work closely with cross-functional teams to identify and implement ML opportunities that drive business value
• Help maintain a balance between risk-focused projects and new ML initiatives

About the key responsibilities

• Develop and refine ML models for risk assessment, fraud detection, and customer experience optimisation
• Explore and implement ML solutions for new business areas, such as customer support automation or merchant onboarding assistance
• Contribute to the team's MLOps practices, improving model deployment and monitoring processes
• Participate in code reviews and knowledge sharing sessions within the team
• Stay informed about the latest ML research and technologies, applying new techniques to solve complex business problems

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

What would make you the perfect fit for the job?

• Education: Master's degree in Computer Science, Machine Learning, Statistics, or a related field
• Experience: minimum 1 year of experience in machine learning or data science (internships or apprenticeships experiences excluded)
• Language: fluent in English mandatory
• Strong programming skills in Python, proficiency with ML libraries such as scikit-learn, TensorFlow, or PyTorch, relational databases, cloud services providers, and knowledge of MLOPs practices and tools
• Solid understanding of ML fundamentals, including supervised and unsupervised learning algorithms, feature engineering, and model evaluation techniques
• Experience with natural language processing (NLP)
• Strong communication & problem-solving skills, methodical, and ability to explain complex concepts to non-technical stakeholders

What would make you stand out of the crowd (the Nice-to-Have)?

• Experience in managing a full ML lifecycle (until monitoring) is highly appreciated
• Familiarity with cloud platforms (preferably GCP) and containerisation technologies (e.g., Docker)
• Familiarity with Generative AI (GenAI) technologies and large language models (LLMs) and their applications in enhancing productivity and decision-making processes
• Experience interacting with graph databases
• Familiarity with financial services, risk modelling, or fraud detection
• Fluency in French

About the recruitment process

• Phone interview with Recruiter (30 mins)
• Technical interview with Lead MLE & 2 MLEs (60 mins)
• Live Applied ML / Python interview & ML system design interview with MLE team (75 mins)
• Final interview with Head of Data (45 mins)