Senior Machine Learning Engineer

Mangopay•Warsaw, IN

About The Position

Mangopay is seeking a Senior Machine Learning Engineer to join its Data Science team and develop and operate machine learning solutions for fraud prevention. This hands-on, end-to-end role combines Data Science with Machine Learning Engineering and Data Engineering, covering the full lifecycle of ML solutions—from sourcing and preparing data, through model development, to deployment and ongoing production maintenance. The team is looking for someone with a strong Data Science foundation and practical experience in either Machine Learning Engineering or Data Engineering, with interest in developing expertise in the other area. The role offers ownership of solutions and the opportunity to shape technical practices while growing engineering expertise. The work focuses on fraud prevention and other machine learning problems with direct business impact, alongside building tools and automations that improve organizational efficiency.

Requirements

At least 5 years of professional experience in Data Science, applied Machine Learning, or a closely related field.
Strong understanding of machine learning algorithms, statistics, feature engineering, model evaluation, and validation techniques.
Excellent Python skills, including experience with pandas, NumPy, and scikit-learn.
Strong SQL skills and experience working with large, complex datasets.
Good software engineering practices, including clean code, testing, version control, and maintainability.
Solid understanding of software architecture and design principles, with the ability to build maintainable and extensible solutions.
Practical experience in at least one of the following: deploying, monitoring, and maintaining production ML models and understanding ML lifecycle management and deployment practices; or building and maintaining data pipelines, data transformations, and reliable datasets using modern data platforms.
Willingness and ability to develop expertise in whichever of Machine Learning Engineering or Data Engineering is less familiar.
Strong analytical and problem-solving skills, including the ability to independently navigate complex technical challenges.
Pragmatic business mindset and excellent communication skills.
Very good English and Polish language skills.

Nice To Haves

Experience with AWS, particularly Amazon SageMaker.
Experience with Kubernetes, Docker, and CI/CD for deploying and operating production services.
Experience with Apache Kafka or other event streaming technologies.
Familiarity with Terraform or other Infrastructure as Code tools.
Experience with Snowflake, dbt, or similar data transformation and warehouse technologies.
Experience building workflow automations with tools such as n8n.
Experience developing AI agents or applying generative AI to business problems.
A degree in mathematics, computer science, quantitative methods, econometrics, or engineering; not mandatory.
Experience in fintech, banking risk, credit, or fraud detection.

Responsibilities

Build and improve fraud detection solutions to identify fraudulent payments, identity theft, and account takeover.
Investigate fraud patterns in transactional and behavioural data, identify suspicious activity, and turn findings into actionable insights.
Research algorithms and data sources, and develop, evaluate, and maintain machine learning models that process millions of transactions.
Build and maintain data pipelines and datasets for feature engineering, model training, and real-time inference.
Develop and operate production ML services, ensuring reliability, scalability, performance, and security.
Build internal tools and workflow automations to streamline processes and improve organizational efficiency.
Lead end-to-end data science projects, from identifying business opportunities and designing solutions to delivering measurable impact on products and clients' bottom line.

Stand Out From the Crowd

Upload your resume and get instant feedback on how well it matches this job.

Upload and Match Resume