Senior Machine Learning Engineer

Teya

Senior Machine Learning Engineer building predictive models to enhance business outcomes at Teya, a payment service provider. Collaborate across teams to integrate models into workflows.

Posted 7/5/2026full-timeRio de Janeiro • 🇬🇧 United KingdomSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates strong foundations in statistics and machine learning, with proficiency in Python and SQL for building and deploying predictive models. Capable of communicating quantitative findings effectively to diverse audiences while applying modern engineering practices and optimization methods.

Highest-signal resume keywords

Machine Learning Model DeploymentStatistical AnalysisPython ProgrammingA/B TestingCloud Platforms

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Hard Skills

StatisticsMachine LearningSupervised LearningUnsupervised LearningGradient BoostingRegularised RegressionClusteringCausal EstimationData Pipeline EngineeringModel Evaluation

Soft Skills

CommunicationCollaboration

Tools & Technologies

Python EcosystemPandasScikit-LearnNumPySQLCI/CDAPIsInfrastructure as Code

Industry Keywords

Predictive ModelingRisk MonitoringCustomer OnboardingData QualityModel Validation

Tech Stack

Tools & technologies

CloudNumpyPandasPythonScikit-LearnSQL

About the role

Key responsibilities & impact

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

What you’ll need

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 business implications clearly to both technical and non-technical audiences.

Benefits

Comp & perks

continuous learning opportunities
supportive community
comprehensive benefits