What do we need:
- Experience in building and deploying machine learning models.
- Proficiency in Python and machine learning frameworks (e.g., TensorFlow, PyTorch).
- Ability to work with LLMs
- Experience with cloud-based environments like GCP
- Experience in using data pipelines for model training and inference.
- Knowledge of statistical techniques and model evaluation metrics.
Nice to Have:
- Experience with Graph Neural Networks (GNNs) or other advanced deep learning architectures.
- Experience in developing scalable data science solutions for real-time applications.
- Knowledge of Google Cloud Platform (GCP) services for machine learning and model deployment.
- Experience working on projects related to fraud detection, risk analysis in financial services.
Responsibilities:
- Design, develop, and deploy machine learning models for fraud detection and behavioral analysis.
- Collaborate with cross-functional teams, including data engineers, product managers, and software engineers, to integrate models into production.
- Research and implement new machine learning algorithms and techniques that push the boundaries of existing models.
- Monitor the performance of models in production, optimizing them for scalability and efficiency.
- Stay up-to-date with the latest trends in machine learning and data science to continuously drive innovation within the team.
Our Interview Process:
- Online technical assessment
- Technical interview
- Cultural interview
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