Teya
Senior Machine Learning Engineer
Rol remoto de Senior Machine Learning Engineer con fit claro de ubicación del candidato.
Publicado6 jul 2026
Países elegibles39 países aceptados
Señal de senioritySenior
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Resumen del rol
Senior Machine Learning Engineer
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
Your Mission
- Frame ambiguous business problems as well-posed modeling, inference, or optimization tasks, and choose methods that fit the data and the decision.
Your Mission
- 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.
Your Mission
- 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.
Your Mission
- Engineer features and build the data pipelines that feed training and serving, with attention to leakage, reproducibility, and data quality.
Your Mission
- Productionise models with strong attention to validation, backtesting, monitoring, drift detection, and retraining, so performance holds up after launch.
Your Mission
- Work closely with product managers, engineers, and domain experts to identify where modeling creates value and to integrate models into products and operational workflows.
Your Mission
- Apply optimization and operations research methods where decisions, not just predictions, are the goal.
Your Mission
- Contribute to modeling standards, evaluation practices, and reusable tooling across the team.
Your Mission
- 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.
Requirements
- Proficiency in Python and its data and ML ecosystem (for example pandas, scikit-learn, NumPy), and strong SQL.
Requirements
- Hands-on experience building and deploying machine learning models in production, not only in notebooks.
Requirements
- 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.
Requirements
- Experience with experimentation and inference, including A/B testing and the basics of causal estimation.
Requirements
- Experience with cloud platforms and modern engineering practices (CI/CD, APIs, monitoring, infrastructure as code).
Requirements
- Strong software engineering fundamentals including testing, reproducibility, and maintainability.
Requirements
- Ability to communicate quantitative findings and their business implications clearly to both technical and non-technical audiences.
Nice to haves
- Experience building models in regulated industries such as payments, fintech, banking, risk, compliance, or fraud prevention.
Nice to haves
- Experience with use cases such as:
Nice to haves
- Fraud detection and anomaly detection
Nice to haves
- Credit and onboarding risk decisioning
Nice to haves
- Pricing and customer lifetime value modeling
Nice to haves
- Churn and propensity modeling
Nice to haves
- Forecasting and time series
Nice to haves
- Recommendation and personalisation
Nice to haves
- Background in operations research, mathematical programming, or stochastic optimization.
Nice to haves
- Knowledge of MLOps, model lifecycle management, feature stores, monitoring, and governance.
Nice to haves
- Experience with deep learning frameworks such as PyTorch or TensorFlow where the problem warrants them.
Nice to haves
- Familiarity with data engineering concepts, analytics platforms, and experimentation frameworks.
Nice to haves
- Contributions to the ML or statistics community through open source, research, or technical writing.
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