Senior Data Scientist, Bugcrowd

• 5+ years of experience in Data Science, Machine Learning Engineering, or Data Engineering
• Deep experience with Python, AWS services (S3, Lambda, Batch, Glue, Bedrock, Step Functions, Redshift), and ML frameworks (Scikit-Learn, XGBoost, PyTorch, etc.)
• Proven experience building end-to-end ML pipelines — from data ingestion to model deployment and monitoring
• Strong understanding of LLM technologies, RAG architectures, and API integration with AI systems
• Ability to design and manage data architectures for large-scale, multi-tenant environments
• Experience applying ML or automation to security or operational intelligence domains
• A builder’s mindset — passionate about shipping scalable, practical AI systems
• The ideal candidate must be able to complete all physical requirements of the job with or without reasonable accommodation
• Sitting and/or standing - Must be able to remain in a stationary position 50% of the time
• Carrying and /or lifting - Must be able to carry / move laptop as needed throughout the work day
• Environment - remote, work-from-home 100% of the time

Desirable

• We’re looking for a hands-on Senior Data Engineer / Data Scientist to design and implement data-driven and AI-powered systems that enhance our offensive security capabilities.
• You’ll build scalable pipelines, deploy intelligent agents, and apply Generative AI, retrieval-augmented generation (RAG), and predictive modeling to solve complex security challenges.
• This role is ideal for someone who thrives at the intersection of AI innovation, data engineering, and cybersecurity, and wants to shape the next generation of intelligent offensive security tools on top of our MCP server and related platforms
• Design, develop, and deploy LLM- and RAG-powered applications that enhance analyst and hacker productivity across offensive security use cases
• Integrate Generative AI models (e.g., via AWS Bedrock, OpenAI, Anthropic) with internal APIs and security datasets to automate and augment workflows
• Build and fine-tune ML models for vulnerability prediction, triage prioritization, and exploit pattern detection
• Develop evaluation pipelines and feedback loops to continuously improve AI model performance and explainability
• Architect and maintain large-scale, high-performance data pipelines to process vulnerability, asset, and activity datasets from multiple sources
• Build secure data ingestion, transformation, and storage workflows leveraging AWS (Glue, Lambda, Step Functions, S3, Redshift, Bedrock) and modern MLOps practices
• Develop robust CI/CD pipelines for data and ML model deployment using AWS CDK and testing frameworks
• Partner with infrastructure teams to scale AI workloads efficiently and securely across multi-tenant environments (FedRAMP, SOC2)
• Collaborate with security researchers and engineers to translate offensive security workflows into data-driven automation
• Integrate ML and AI systems with core security platforms such as the MCP server, Bugcrowd Connect, and vulnerability intelligence pipelines
• Design APIs and interfaces that enable LLM agents to interact with internal systems for search, enrichment, and decision support
• Work cross-functionally with data, product, and platform teams to drive adoption of AI capabilities across the engineering organization
• Provide technical mentorship and guide best practices for ML infrastructure, feature engineering, and model observability
• Contribute to architectural reviews, ensuring scalability, maintainability, and security in all AI and data systems