Senior AI Data Scientist
Chaos Labs builds technology for safer financial markets, including risk management systems, oracles, and AI models for blockchain protocols and financial institutions. It also develops enterprise AI infrastructure for understanding, governing, and improving organizational AI work.
Funding history
Projects
About Chaos Labs
Chaos Labs provides risk management, oracle, proof-of-reserves, analytics, and AI infrastructure for blockchain protocols, exchanges, institutions, and other financial platforms. Its services include simulation, testing, and analysis tooling for blockchain protocols, as well as price oracles and onchain monitoring that help secure financial markets and digital assets. The company also builds enterprise AI infrastructure focused on visibility into AI workflows, organizational knowledge, governance, and proprietary intelligence.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Design and build machine learning systems for search, retrieval, recommendations, and AI agents; develop evaluation frameworks, scalable data and ML pipelines, statistical models, and AI capabilities while analyzing product behavior, defining KPIs, guiding strategy, collaborating cross-functionally, and mentoring junior data scientists.
Requirements
- 5+ years of experience in Data Science, Applied Machine Learning, or quantitative research, or 3+ years with a PhD
- Degree in Computer Science, Statistics, Mathematics, Machine Learning, Economics, Physics, or another highly quantitative field
- Strong proficiency in Python and SQL
- Experience building scalable data and machine learning pipelines
- Deep understanding of statistics, experimentation, causal inference, and predictive modeling
- Experience developing and deploying machine learning models in production
- Experience working with large-scale datasets and modern data infrastructure
- Familiarity with LLMs, embeddings, retrieval systems, RAG, or AI agents
- Strong analytical thinking
- Excellent communication and cross-functional collaboration skills
Responsibilities
- Design and build machine learning systems for enterprise search, retrieval, recommendations, and AI agents
- Develop evaluation frameworks and metrics for LLM-powered applications
- Build scalable data and ML pipelines for large-scale AI interaction data
- Apply statistical modeling, experimentation, and causal inference to guide product development and strategic decisions
- Collaborate with Product, Engineering, and Design to translate research into production features
- Analyze product usage and customer behavior to improve AI adoption, productivity, and user experience
- Prototype, evaluate, and deploy AI capabilities using foundation models, embeddings, and RAG
- Define key performance indicators and analytical frameworks
- Identify opportunities to leverage machine learning and generative AI
- Mentor junior team members and establish data science and machine learning best practices
Benefits
- Equity compensation
- Professional growth opportunities
Hiring Process
Hiring Process typically takes two to four weeks: interview with Talent Lead, technical assignment, interview with Hiring Manager, and interview with CEO.