Senior AI Data Scientist

Skills

About the Role

You will lead the design, evaluation, and evolution of agentic systems that reason, plan, and act over complex financial data. You will define agent behavior, memory, and tool-use strategies focused on correctness and controllability. You will build and maintain LLM evaluation frameworks, design prompting and schema strategies, operate MCP servers, analyze model failures, and partner with engineering to productionize observability, retries, state, and reliability. You will also mentor engineers and data scientists and help set best practices for applied LLM and agentic systems.

Requirements

• 6+ years experience in data science, applied ML, or AI-focused software roles
• 3+ years building production AI or ML systems with ownership beyond experimentation
• Deep hands-on experience with LLMs, agentic patterns, and tool-calling systems
• Strong Python skills and comfort working close to production systems and APIs
• Experience with RAG pipelines, embeddings, and vector databases
• Strong intuition for model behavior, trade-offs, and failure analysis
• Experience applying ML or statistical methods to financial domains (crypto/DeFi a plus)
• Track record of building reusable ML or data abstractions that improved team velocity or decision-making

Responsibilities

• Design and own single and multi-agent systems that reason, plan, and act over complex financial workflows
• Define agent behavior, memory, and tool-use strategies with emphasis on correctness and controllability
• Develop and maintain LLM evaluation frameworks covering accuracy, faithfulness, latency, cost, regressions, and edge cases
• Design structured prompting, schemas, and tool-calling strategies for production LLM systems
• Build and operate MCP servers including schema design, permissions, and safety boundaries
• Analyze model behavior and failure modes and convert qualitative issues into measurable signals
• Partner with engineering to productionize research insights including observability, retries, state, and reliability
• Optimize system performance and cost across models, workflows, and agent architectures
• Mentor engineers and data scientists and set best practices for applied LLM and agentic systems

Benefits

• Paid time off: 21 vacation days, 7 sick days, and 8 observed U.S. company holidays
• Health coverage: employer-paid options for medical, dental, and vision for employees and dependents
• FSA or HSA options depending on selected health plan
• Parental leave policy
• Wellness programs (OneMedical, Teladoc, Talkspace, and EAP)
• Pre-tax commuter benefits
• Competitive compensation and equity package