Joining a multi-skilled team of Data Scientists & Engineers, you will utilise yourData Scienceskills todesign, build and deploy machine learning solutions. Our tech stack is based in GCP and our Engineering & Product Teams are co-located in our London office - ensuring you will have direct access to decision makers and colleagues to drive projects forward quickly, with collaboration playing a key role.

Our Teams are empowered to push the boundaries of the impact AI can have within a scaling, multi-product FinTech organisation - with autonomy and independence in abundance at Liberis. Your role will be responsible for developing scalable ML systems while collaborating with peers and contributing independently to the success of our projects.

What you'll get to do:

• Design, develop, and deploy end-to-end machine learning systems in Python, ensuring reliability, scalability, and performance.
• Collaborate with data scientists and engineers to integrate machine learning models into production systems, focusing on the quality and maintainability of solutions.
• Work independently to address technical challenges in machine learning pipelines and model deployment.
• Collaborate closely with cross-functional teams and communicate technical concepts effectively to both technical and non-technical stakeholders.

Interview process:

1. Screening call with Chess (Internal recruiter)
2. Video interview with the Hiring Manager
3. Tech interview with the ML & AI Team (project discussion)
4. Tech interview with the ML & AI Team (skills discussion)
5. Interview with the Engineering Manager

What you'll bring:

• Hands-on experience in an ML engineering role, with a track record of developing and deploying machine learning models in production.
• Strong expertise in Python, including data analysis libraries such as Pandas and Numpy, and machine learning frameworks like PyTorch or TensorFlow.
• Candidates must have experience in either forecasting (e.g., revenue forecasting, time series modeling) or risk-based modeling (e.g., Probability of Default, credit risk metrics), as this role requires expertise in at least one of these critical areas to drive data-driven decisions.
• Deep understanding of machine learning concepts, including optimisation, statistics, and algorithm development.
• Experience in building and maintaining cloud-based machine learning services, preferably using GCP or other cloud platforms.
• Solid understanding of classical ML algorithms (e.g., Logistic Regression, Random Forest, XGBoost) and modern deep learning techniques (e.g., LSTM).

Next Steps:

If this opportunity feels like the right fit for your next career move, we’d love to hear from you! Even if you don’t meet every requirement, don’t hesitate to apply or reach out to Chess (Internal Recruiter) at .

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