• Designing and enhancing risk-based analytic models to identify high-risk providers, services, and utilization pattern • Conducting exploratory and targeted data analyses to support CMS program integrity initiatives • Supporting development of dashboards, visualizations, and reporting products for CMS leadership and technical staff • Collaborating with the prime contractor and CMS to refine analytic methodologies, assumptions, and outputs • Translating analytic results into clear findings, documentation, and recommendations usable by non-technical audiences

• Lead complex analytics initiatives requiring advanced scripting and analytical methods. • Design, build, and maintain analytical solutions using SQL, Python, and R. • Contribute across the full analytics lifecycle: discovery, design, development, validation, and refinement. • Define and enforce enterprise-wide analytics and coding standards, including governance of citizen-developed solutions. • Conduct code reviews to ensure quality, performance, maintainability, and alignment with standards. • Develop reusable Python and R packages, shared SQL patterns, and analytical frameworks. • Support business partners with advanced analytical needs beyond standard BI reporting. • Apply advanced statistical and analytical techniques to solve complex business problems. • Selectively apply AI/ML techniques where they deliver clear business value. • Collaborate with Data Engineers and Architects to ensure alignment with data models, pipelines, and governance. • Clearly communicate insights, tradeoffs, and recommendations to technical and non-technical stakeholders. • Stay current with emerging analytics technologies and assess applicability.

• Develop end-to-end AI solutions, from discovery through deployment and monitoring, including modeling (NLP/ML/LLMs), APIs, and integrations. • Design and evolve chatbots and conversational channels using NLP/NLU, driving continuous improvement. • Build integrations via APIs/JSON, microservices, and event-driven architectures, and implement MLOps with pipelines, versioning, and observability. • Analyze conversational data and recommend actions based on KPIs, ensuring quality through testing and validation. • Work in agile methodologies, collaborating with business, design, engineering, and security teams.

Chaos Labs builds technology that powers safer, more accessible financial markets. Our risk management systems, oracles, and AI models secure over $200 billion in assets across protocols including Aave, Jupiter, and GMX, processing more than $5 trillion in transaction volume. We recently launched Chaos AI, a financial intelligence platform that transforms complex market data into actionable insights and brings institutional-grade intelligence to everyone. The Role: We’re looking for a Senior AI Data Scientist to lead the design, evaluation, and evolution of the agentic systems behind Chaos AI. This role sits at the intersection of LLMs, financial data, and decision systems. You’ll own how models reason over data, how agents coordinate and make decisions, and how we rigorously measure quality, correctness, and risk in production AI workflows. You’ll work closely with product, engineering, and research to move from experimentation to reliable, scalable AI systems that operate under real financial constraints. 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 a strong 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, turning qualitative issues into measurable signals Partner with engineering to productionize research insights: observability, retries, state, and reliability Optimize system performance and cost across models, workflows, and agent architectures Mentor engineers and data scientists, setting best practices for applied LLM and agentic systems 6+ years experience in data science, applied ML, or AI-focused software roles 3+ years building production AI / 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 Preferred Qualifications: Our Perks: Paid Time Off - 21 vacation days + 7 sick days + 8 observed U.S. company holidays Health Coverage - 100% employer-paid options (medical, dental & vision) for you and your dependents FSA / HSA Options - depending on selected health insurance plan Parental Leave - thoughtful policy to support your family needs Wellness Programs - OneMedical, Teladoc, Talkspace, and EAP Career Growth - opportunities in a rapidly expanding, global technology company with personalized professional development Pre-tax Commuter Benefits Compensation & Equity - competitive package aligned with growth and merit Salary and seniority level commensurate with experience (Range: $130K–$150K)