Staff Data Science Engineer

Role Overview

• Design, implement, and deploy advanced mathematical and machine-learning algorithms to support cyber-range simulations.
• Develop and maintain end-to-end AI/ML pipelines.
• Construct and optimize numerical methods and computational models using Python, NumPy, SciPy, Pandas, and JAX/TensorFlow/PyTorch.
• Architect scalable model-serving systems in Docker/Podman/Kubernetes.
• Develop and integrate new AI-driven cybersecurity capabilities.
• Author and maintain production-quality Python services.
• Design, evaluate, and improve model performance using quantitative metrics.
• Perform algorithmic research on emerging ML/AI/cyber methods.
• Lead cross-team technical initiatives.
• Mentor senior-level engineers and data scientists.

Requirements

• Ph.D. in Computational Mathematics, Computer Science, Applied Mathematics, or a closely related field.
• 1 year of experience in computational mathematics, scientific computing, machine learning, data science, or algorithm development.
• Demonstrated experience applying machine-learning algorithms to datasets of at least 1 million observations or high-dimensional data.
• Demonstrated experience developing scientific or ML software in Python using at least three of the following packages: NumPy, Pandas, SciPy, Matplotlib.
• Demonstrated experience implementing machine-learning models using at least three of the following frameworks: PyTorch, TensorFlow, JAX, scikit-learn.
• Demonstrated experience writing automated tests for ML or scientific code using at least two of the following: unittest, pytest, hypothesis.
• Demonstrated experience building and deploying containerized applications using at least one of the following: Docker, Podman, Kubernetes.
• Demonstrated experience producing documented research or production-quality software artifacts.
• Demonstrated experience applying computational mathematics methods to design or evaluate algorithms or models, with documented quantitative results.
• Demonstrated understanding of statistics, computational complexity and performance, parallelization, databases, optimization, linear programming, hypothesis testing.

Tech Stack

• Cyber Security
• Docker
• Kubernetes
• Numpy
• Pandas
• Python
• PyTorch
• Scikit-Learn
• Tensorflow

Benefits

• In-house training
• Internal and external learning platforms
• Cyber conferences
• Industry events
• Dedicated time for skill development