Senior Machine Learning Engineer in Financial Services

You will lead the design and development of credit-scoring, risk-management, and marketing models for financial services. You will define business and quality requirements, design machine-learning system architectures, guide engineering teams through product integration, and improve MLOps for training, evaluation, monitoring, and continuous improvement.

Responsibilities

• Design and develop credit, risk-management, and marketing models
• Define business requirements and select suitable prediction models
• Design system architectures for AI and machine-learning applications
• Define requirements for prediction accuracy, explainability, stability, security, and operating cost
• Integrate models safely into products and business processes
• Lead development direction, code reviews, and testing strategies
• Collaborate with engineers, data scientists, and data analysts
• Update MLOps architecture for model training, evaluation, monitoring, and continuous improvement
• Drive operational improvements across financial-services machine-learning systems

Requirements

• Advanced expertise in AI, machine learning, mathematical engineering, or mathematical statistics
• At least approximately three years of practical experience developing and operating machine-learning prediction models
• Experience developing models for credit scoring, risk management, fraud detection, marketing optimization, or user behavior prediction
• Experience defining machine-learning system quality requirements
• Experience leading end-to-end system architecture design and product or process integration
• Team software development and leadership experience using Python or similar technologies
• Financial or payments experience
• PhD in mathematical engineering, statistics, computer science, or a related field
• Experience with large-scale data processing pipelines on GCP, AWS, or similar cloud environments
• Experience introducing or operating MLOps platforms such as Kubeflow or MLflow
• Experience leading teams of approximately 5 to 10 people

Benefits

• Hybrid workstyle
• Super flextime with no core hours
• Annual paid leave of 14 days in the first year
• Personal leave of 5 days annually
• Health insurance
• Employees' pension insurance
• Employment insurance
• Workers' compensation insurance
• Corporate defined-contribution pension plan