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Amazon Applied Scientist Global Services Security - Office Innovation 
United States, Kansas 
752112918

12.08.2024
DESCRIPTION

Key job responsibilities
* Interact with security engineers, product managers and related domain experts to dive deep into the types of opportunities we have for innovative solutions.
* Rapidly design, prototype, and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment.
* Invent, implement, and deploy state-of-the-art machine learning algorithms and systems for information security applications.
* Collaborate with software engineering teams to integrate successful experiments into large-scale, highly complex production services.
* Report results in a scientifically rigorous way.Work/Life Balance
Mentorship & Career Growth

BASIC QUALIFICATIONS

- PhD or equivalent experience in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field
- 3+ years of working knowledge of deep learning
- 2+ years of hands-on experience in predictive modeling and analysis
- 2+ years of algorithm development experience
- 2+ years of coding with at least one of the following: Java, C++, or other programming language; Additionally R, MATLAB, Python or similar scripting language


PREFERRED QUALIFICATIONS

- Hands-on experience with a broad set of ML approaches and techniques - possibly including traditional classification and clustering, time series analysis, anomaly detection, artificial neural networks, and Bayesian non-parametric methods
- Hands-on experience building models with deep learning frameworks like MXNet, Tensorflow, Keras, Caffe, PyTorch, or similar. Prior experience training and fine-tuning Large Language Models (LLMs)
- Authored peer-reviewed academic publications
- Extensive experience applying theoretical models to production applications
- Experience in production level software development including mechanisms such as CI/CD, infrastructure-as-code, agile development, containerization, serverless