Senior Machine Learning Engineer

Mangopay is awallet-based payment infrastructure built specifically for organisations with complex, multi-party fund flows. A pioneer in multi-party payments.

Our solution optimises fund flows on behalf of the organisations we work with using wallets asprogrammable, composable building blocks.

Mangopay’sregulated platform collects payments, secures transactions and holds funds, splits money between the various parties in the funds flow, and ultimately manages the payout to service providers, sellers, and consumers.

Platforms and fintechs using Mangopay regain control and transparency overmulti-party payment flows, generate additional revenue, and improve operational efficiency. They can stay compliant while innovating and scaling.

Job Description

The opportunity

We are looking for a Senior Machine Learning Engineer to join our Data Science team and help develop and operate machine learning solutions for fraud prevention.

This is a hands-on, end-to-end role combining Data Science with Machine Learning Engineering and Data Engineering. You will work across the entire lifecycle of ML solutions, from sourcing and preparing data through model development to deployment and ongoing maintenance in production.

We don't expect you to be an expert in all three disciplines. We're looking for someone with a strong Data Science foundation and practical experience in either Machine Learning Engineering or Data Engineering, who is eager to grow their skills in the other area.

You will have the opportunity to take ownership of solutions, shape our technical practices, and grow your engineering expertise while working on problems with direct business impact.

What you will do

• Build and improve fraud detection solutions that help identify fraudulent payments, identity theft, and account takeover.

• Investigate fraud patterns by exploring transactional and behavioural data, identifying suspicious activity, and turning findings into actionable insights.

• Research new algorithms and data sources, and develop, evaluate, and maintain machine learning models that process millions of transactions.

• Build and maintain data pipelines and datasets supporting feature engineering, model training, and real-time inference.

• Develop and operate production ML services, ensuring their reliability, scalability, performance, and security.

• Build internal tools and workflow automations to streamline processes and improve efficiency across the organization. Automation is close to our hearts!

• Lead end-to-end data science projects, from identifying business opportunities and designing solutions to delivering measurable impact on our products and clients' bottom line.

What you bring

• 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 the Python scientific stack: 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 areas:

  • Machine Learning Engineering: deploying, monitoring, and maintaining ML models in production, with an understanding of ML lifecycle management and deployment practices.

  • Data Engineering: building and maintaining data pipelines, data transformations, and reliable datasets using modern data platforms.

• Willingness and ability to develop expertise in whichever of these two areas is less familiar to you.

• Strong analytical and problem-solving skills, with the ability to independently navigate complex technical challenges.

• A pragmatic business mindset and excellent communication skills.

• Very good English and Polish language skills.

Nice to have

• 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 (IaC) 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.

Why join us?

You will work on machine learning problems with direct business impact, taking ownership of solutions from initial exploration through production.

This is an opportunity to combine Data Science with real-world engineering, deepen your expertise in ML or Data Engineering, and help shape how our data and ML systems are developed and maintained.

We value technical curiosity, initiative, pragmatic problem-solving, collaboration, and continuous learning.

Additional info:

• HR call

• Take home test

• Case study interview: presentation to the Hiring manager + VP

• Offer!