Senior Applied Scientist, Perimeter Protection Applied Science
Tasks
- Analyze large scale production data for patterns and anomalies
- Build deploy machine learning systems for mitigation and protection
- Design production machine learning models for real time threat detection
- Develop train and deploy ML pipelines
- Establish best practices for evaluation A B testing monitoring
- Formulate problems and engineer features
- Integrate ML models into distributed low latency services
- Mentor junior scientists and engineers
- Monitor models and optimize performance
- Own end-to-end machine learning lifecycle
Perks/Benefits
- 401k matching
- Dental insurance
- Employee assistance program
- Health insurance
- Paid time off
- Parental leave
- Vision insurance
Skills/Tech-stack
A/B | A/B Testing | B testing | C++ | Data Engineering | Deep learning | Deep learning methods | Distributed Systems | Feature Engineering | Hadoop | Java | Latency optimization | Learning methods | MXNet | Machine Learning | Machine Learning Pipelines | Managed Machine Learning | Model Evaluation | Model Monitoring | Model Serving | Neural Deep Learning Methods | Neural Networks | Neural deep learning | NumPy | Python | R | Reliability Engineering | SciPy | Scikit-learn | Spark | Spark MLlib | TensorFlow | Throughput Optimization | Training Infrastructure
Education
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