AI Data Scientist Engineer

Job Description

Join Us in Transforming Cybersecurity with AI At ThreatModeler, we are on a mission to revolutionize the way organizations approach cybersecurityempowering teams to shift left and proactively design secure systems from the start. Its not just about what we buildits about how we work and grow together. When you join ThreatModeler, you become part of a team that values curiosity, creativity, and collaboration. Here, you'll have the chance to tackle exciting challenges, work with real-world data, and grow alongside industry experts who are passionate about applying AI to solve complex cybersecurity problems. With $60 million in institutional funding from Invictus Growth Partners, were scaling our AI-driven solutions to redefine the future of threat modeling. Responsibilities and Qualifications Collaborate with cross-functional teams to identify opportunities where AI and machine learning can add value to cybersecurity workflows and threat modeling automation. Design, develop, and deploy machine learning models, from proof-of-concept to production-ready solutions. Clean, process, and analyze datasets, including structured and unstructured data. Build AI pipelines based on foundational (e.g. OpenAI) and open-source models (e.g. LLAMA3) Participate in the full agile development lifecycle, including sprint planning, daily stand-ups, and code reviews. Leverage cloud platforms (Azure/AWS/GCP) to scale model training and deployment. Develop APIs and services that expose ML/AI functionality to other components in the platform. Collaborate with software engineers to integrate models into production systems with focus on performance, reliability, and maintainability. Stay up-to-date on the latest trends in AI, deep learning, and cybersecurity threats. Requirements 3 to 5 years of experience applying data science or machine learning in a production environment. General understanding of data science concepts including supervised/unsupervised learning, time series forecasting, NLP, and anomaly detection. Proficiency in Python (with libraries like scikit-learn, pandas, NumPy, PyTorch/TensorFlow) Solid understanding of data structures, algorithms, and software engineering principles. Experience with cloud platforms and tools for ML lifecycle (e.g., Azure ML, AWS SageMaker, Vertex AI) Experience with SQL and NoSQL databases for data access and manipulation. Familiarity with APIs and microservice development. Bachelor’s in Computer Science, Data Science, Applied Mathematics, or a related field. Strong problem-solving and analytical skills; experience working in a cross-functional environment. Bonus: Experience with cybersecurity data or threat intelligence models.