Staff Data Science Engineer
S
SimSpace CorporationCybersecurity
Anywhere in the USFull-TimeStaff
Salary183801 - 184000 USD per year
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Job Details
- Experience
- 1 year of experience
- Required Skills
- DockerPythonDifferential EquationsKubernetesMachine LearningNumpyPyTorchPandasTensorflowscikit-learnNLP
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 mathematical or machine-learning algorithms (e.g., regression, classification, clustering, reinforcement learning, NLP, numerical optimization) 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, training, and validating 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 (e.g., peer-reviewed publications, open-source contributions, internal enterprise algorithms or models) demonstrating algorithm correctness or performance validation.
- Demonstrated experience applying computational mathematics methods (e.g., linear algebra, numerical optimization, differential equations, stochastic processes, network or graph analysis) 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, research methodology, and existing scientific literature and results in the field of data science and AI/ML.
Responsibilities
- Design, implement, and deploy advanced mathematical and machine-learning algorithms to support cyber-range simulations, delivering production models with documented accuracy, latency, and throughput metrics.
- Develop and maintain end-to-end AI/ML pipelines (data ingestion, feature engineering, model training, validation, inference, monitoring), ensuring test coverage, reproducibility of experiments, and documented performance benchmarks.
- Construct and optimize numerical methods and computational models using Python, NumPy, SciPy, Pandas, and JAX/TensorFlow/PyTorch to solve large-scale (10M+ row) data and optimization problems relevant to cyber-range operations.
- Architect scalable model-serving systems in Docker/Podman/Kubernetes, achieving reliable deployments with measured service uptime of 99 percent or greater and documented resource-utilization improvements.
- Develop and integrate new AI-driven cybersecurity capabilities (e.g., automated scoring engines, classification systems, reinforcement-learning-based adversary behaviors) with quantified gains in accuracy, precision/recall, or scenario realism, validated against internal evaluation datasets.
- Author and maintain production-quality Python services, enforcing code standards, implementing unit/integration testing with unittest/pytest, and reducing defect rates via measurable static/dynamic analysis reports.
- Lead cross-team technical initiatives, producing written design documents, conducting architecture reviews, and driving the integration of DS/AI services across engineering, product management, platform teams, and cybersecurity content engineering.
- Mentor senior-level engineers and data scientists by conducting formal code reviews, mathematical model reviews, and algorithm correctness checks, with documented feedback that improves model accuracy, stability, or performance.
- Apply computational mathematics methods (e.g., linear algebra, numerical optimization, differential equations, stochastic processes) to design, implement, and validate algorithms and models with documented quantitative results.
- Define and establish technical standards, best practices, and design patterns for AI/ML development across the Data Science team.
- Drive high-performance computing initiatives to optimize AI/ML system performance, including distributed computing and GPU acceleration strategies.
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