Hello! We're Teya.

Teya is a payment and software service provider, headquartered in London serving small, local businesses across Europe. Founded in 2019, we build easy to use, integrated tools that enable our members to accept payments and boost business performance.

At Teya we believe small, local businesses are the lifeblood of our communities.

We’re here because we don’t believe there’s a level playing field that gives small businesses with a fighting chance against the giants of the high street.

We’re here because we see banks and legacy service providers making things harder for them. We don’t think the best technology or the best service should be reserved for those with the biggest headquarters.

We’re here to fight for a future where small, local businesses can thrive, and to commit the same dedication they offer all of us.

Become a part of our story.

We’re looking for exceptional talent to join our mission. We offer a chance to create impact in a high-energy and connected culture, while benefiting from continuous learning opportunities, a supportive community which is proud to serve our mission, and comprehensive benefits.

Your Mission

As a Senior Machine Learning Engineer you will build the models and decision systems that turn Teya's data into better outcomes for our customers and our business. You will work on problems where quantitative rigor changes the result: who we onboard, how we detect and prevent fraud, how we price, and how we understand and grow customer value.

You'll join our AI Research & Development team, partnering with engineers, product managers, and domain experts to take problems from framing through model development and into reliable, monitored production systems. This role suits someone who combines strong statistical and machine learning foundations with the engineering discipline to ship, and who is motivated by measurable business impact rather than modeling for its own sake.

As a Senior Machine Learning Engineer at Teya, you will be expected to:

Frame ambiguous business problems as well-posed modeling, inference, or optimization tasks, and choose methods that fit the data and the decision.

Design, build, validate, and deploy predictive and decisioning models across areas such as fraud and risk monitoring, customer onboarding and due diligence, pricing, and customer lifetime value.

Run rigorous experiments and causal analyses, including A/B testing, uplift modeling, and offline evaluation, to measure whether models actually move the outcomes that matter.

Engineer features and build the data pipelines that feed training and serving, with attention to leakage, reproducibility, and data quality.

Productionise models with strong attention to validation, backtesting, monitoring, drift detection, and retraining, so performance holds up after launch.

Work closely with product managers, engineers, and domain experts to identify where modeling creates value and to integrate models into products and operational workflows.

Apply optimization and operations research methods where decisions, not just predictions, are the goal.

Contribute to modeling standards, evaluation practices, and reusable tooling across the team.

Stay current with developments in machine learning and statistics, and apply new methods where they earn their place.

Requirements

Strong foundations in statistics and machine learning, with the judgment to match methods to problems.

Proficiency in Python and its data and ML ecosystem (for example pandas, scikit-learn, NumPy), and strong SQL.

Hands-on experience building and deploying machine learning models in production, not only in notebooks.

Solid command of supervised and unsupervised learning, including methods such as gradient boosting, regularised regression, and clustering, with a clear understanding of model evaluation and overfitting.

Experience with experimentation and inference, including A/B testing and the basics of causal estimation.

Experience with cloud platforms and modern engineering practices (CI/CD, APIs, monitoring, infrastructure as code).

Strong software engineering fundamentals including testing, reproducibility, and maintainability.

Ability to communicate quantitative findings and their