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About the Company
ThreatModeler Software, Inc. is an industry leader in automated threat modeling, helping enterprises proactively secure their systems by identifying, quantifying, and mitigating cybersecurity threats during the design phase. We're expanding our AI capabilities to accelerate threat detection, model generation, and decision intelligence — and we're looking for a strategic Senior Data Scientist to join this mission.
About the Role
As a Senior Data Scientist – AI, you will lead the design and development of machine learning and generative AI models that enhance our threat modeling platform. You will work closely with engineers, product managers, and cybersecurity SMEs to innovate how we analyze architectures, assess risks, and automate threat intelligence. This is a hands-on, highly strategic role that requires both technical depth and product insight.
Responsibilities
- Design and implement ML and NLP models for use of pattern recognition, and AI-powered automation.
- Build and evaluate generative AI models (e.g., LLMs or RAG pipelines) to automate model generation and risk recommendations.
- Collaborate with product and engineering to align AI solutions with customer pain points and business goals.
- Conduct data discovery, wrangle large security and architecture datasets, and ensure data quality for training and inference.
- Lead experiments, performance tuning, and model deployment using MLOps best practices.
- Develop reusable components and contribute to internal AI frameworks and libraries.
- Interpret and communicate complex data science results to non-technical stakeholders.
- Mentor junior data scientists and help define the future AI roadmap at ThreatModeler.
Qualifications
- 6-8+ years of industry experience in data science, machine learning, or AI development, preferably in enterprise software or cybersecurity.
- Strong experience in NLP, LLMs (e.g., OpenAI, Cohere, Anthropic), and transformer architectures.
- Proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn, Hugging Face).
- Deep understanding of model evaluation, bias/fairness, data preprocessing, and statistical analysis.
- Familiarity with cloud environments (AWS/GCP/Azure) and containerized model deployment (Docker, Kubernetes).
- Experience working with graph-based models is a strong plus.
Preferred Skills
- Experience with MLOps tools (MLflow, Vertex AI, SageMaker).
- Background in building SaaS AI solutions for large-scale enterprise clients.
- Publications, open-source contributions, or patents in AI or security.
