Senior Machine Learning Engineer

Teya está com vaga(s) de emprego para Senior Machine Learning Engineer em Rio de Janeiro / RJ

Cargo:

Senior Machine Learning Engineer – rio de janeiro

Senior Machine Learning Engineer – Teya Senior Machine Learning Engineer you will build models and decision systems that turn Teya's data into better outcomes for our customers and our business. You will work in the 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. Responsibilities 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 (e.g., 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. Nice to have Experience building models in regulated industries such as payments, fintech, banking, risk, compliance, or fraud prevention. Experience with use cases such as: Fraud detection and anomaly detection Credit and onboarding risk decisioning Pricing and customer lifetime value modeling Churn and propensity modeling Forecasting and time series Recommendation and personalisation Background in operations research, mathematical programming, or stochastic optimization. Knowledge of MLOps, model lifecycle management, feature stores, monitoring, and governance. Experience with deep learning frameworks such as PyTorch or TensorFlow where the problem warrants them. Familiarity with data engineering concepts, analytics platforms, and experimentation frameworks. Contributions to the ML or statistics community through open source, research, or technical writing. Equal Opportunity Employer Teya is proud to be an equal opportunity employer. We are committed to creating an inclusive environment where everyone, regardless of race, ethnicity, gender identity or expression, sexual orientation, age, disability, religion, or background can thrive and do their best work. If you require any reasonable adjustments at any stage of the recruitment process, please let us know – we are committed to ensuring a fair and accessible experience. #J-18808-Ljbffr

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