Machine Learning Engineer
November 10
Description
• Alma is seeking a talented Machine Learning Engineer to join our expanding AI/ML team within the Data department. • As we broaden our ML capabilities across the organisation, we are looking for an enthusiastic individual to contribute to our core risk assessment models and help drive innovation in new areas of the business. • In this role, you will: • 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. • Develop and refine ML models for risk assessment, fraud detection, and customer experience optimization. • 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.
Requirements
• Master's degree in a relevant field (computer science, machine learning, mathematics, engineering, statistics, etc.) • Minimum 1 year of full-time permanent experience (internships & apprenticeships excluded) in machine learning or data science • Strong communication skills and ability to explain complex concepts to non-technical stakeholders • Fluency in English is mandatory. Fluency in French will be a plus! • Solid understanding of ML fundamentals, including supervised and unsupervised learning algorithms, feature engineering, and model evaluation techniques • Strong programming skills in Python and proficiency with ML libraries such as scikit-learn, TensorFlow, or Pytorch • Experience with natural language processing (NLP) • Knowledge of MLOps practices and tools • Familiarity with cloud platforms (preferably GCP) and containerization 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
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