Applied Data Scientist

3-5 years of experience in applied data science, ML product work, or security-focused AI, including technical leadership or staff-level ownership. Designed and evaluated real-world ML systems with a focus on model behavior, error analysis, and continuous improvement. Can design red teaming workflows to surface model blind spots and failure modes. Operates effectively across ML, infra, and policy / strategy contexts. Degree (or equivalent work experience) in Data Science, Information Science, Computer Science with ML focus, or a related field (graduate degree preferred). Background in data science, applied ML, or ML engineering, with proven experience in production-grade systems. Strong analytical toolkit (Python, SQL, Jupyter, scikit-learn, Pandas, etc.) and familiarity with modern ML tooling (e.g., PyTorch, Hugging Face, LangChain). Experience working with LLMs and embedding-based classification systems. Excellent communication skills across strategy and technical domains. Comfort working in fast-moving, high-impact environments, such as startups, AI research labs, or security-focused teams.

Design the technical implementation of a robust red teaming project. Lead adversarial testing efforts (e.g., red teaming, evasion probes, jailbreak simulation) and analysis efforts. Work with researchers and domain experts to define labeling schemas and edge-case tests. Partner with ML and infrastructure engineers to ensure production readiness, observability, and performance targets. Communicate technical strategy and tradeoffs clearly across internal and client teams. Automate red teaming, including developing automated workflows for prompt generation, model evaluation, and execution of AI experiments; fine-tune LLMs or classification systems. Brainstorm novel research approaches to both known and emerging problems involving AI, data, and the internet.