Key Responsibilities
- Own product analytics end-to-end, from event design and instrumentation through data modeling, analysis, and product recommendations.
- Define and maintain a consistent event taxonomy and shared metrics layer across Hermes Agent and Nous Portal.
- Implement and maintain analytics instrumentation across desktop, mobile, web, CLI, backend services, and third-party integrations.
- Build reliable data pipelines, transformations, dashboards, and monitoring systems that make product behavior easy to understand and trust.
- Analyze user funnels, cohorts, retention, behavioral paths, and churn to identify friction points and opportunities for improvement.
- Segment user behavior across platforms, acquisition channels, pricing plans, models, tools, and use cases to uncover actionable insights.
- Design and evaluate experiments, measuring the impact of product changes on activation, engagement, retention, conversion, and revenue.
- Partner with Product, Engineering, Design, and Support to translate data into prioritized product improvements.
- Combine quantitative analysis with qualitative feedback from users, customer conversations, and direct product usage to develop a complete understanding of user behavior.
- Build monitoring and alerting systems for key product metrics and proactively investigate anomalies.
- Establish best practices for analytics instrumentation, documentation, data quality, and privacy-conscious telemetry.
Qualifications & Requirements
- 5+ years of experience in Product Analytics, Analytics Engineering, Data Engineering, or a similarly technical product role.
- Expert SQL skills and strong proficiency in Python, with experience working in production application codebases using TypeScript, JavaScript, or similar languages.
- Hands-on experience implementing analytics instrumentation across client applications and backend systems.
- Experience with modern product analytics platforms such as PostHog, Amplitude, or Mixpanel, along with data warehouses and BI tools.
- Deep understanding of product metrics including activation, engagement, retention, churn, experimentation, and funnel analysis.
- Strong analytical and problem-solving skills with the ability to trace issues from dashboards through underlying systems and instrumentation.
- Excellent communication skills and the ability to translate complex behavioral data into clear product recommendations.
- Entrepreneurial mindset, strong ownership, and the ability to operate effectively in a fast-moving environment.
- Extensive use of AI-assisted development and analysis to improve speed, depth, and quality of work.
Preferred
- Experience working on AI products, LLM applications, AI agents, or developer tools.
- Familiarity with agent workflows, tool calling, model routing, memory systems, and other modern AI application patterns.
- Experience measuring cross-platform products spanning desktop, mobile, web, CLI, and enterprise deployments.
- Experience with subscription or usage-based SaaS products, including conversion, credits, renewals, and monetization.
- Experience designing privacy-conscious telemetry and analytics systems for open-source or developer-focused products.
- Experience building analytics systems that support high-volume event streams and rapidly growing products.
About Nous Research
Nous Research, founded in 2023 by a distributed collective of AI researchers and open-source contributors, builds decentralized AI models and training infrastructure for open, aligned intelligence. Its work includes open-weight Hermes models, Psyche decentralized training that coordinates global GPU contributions, and inference tooling that lets developers run models without centralized APIs. Headquartered with a fully distributed team across North America and Europe, Nous emphasizes open research, model evaluation and community-driven alignment over proprietary development. The project collaborates with open-source communities and crypto incentive designers to fund compute and data contributions transparently. Its collective publishes evaluations, datasets and training playbooks that let open-source contributors reproduce, fine-tune and deploy models with transparent provenance. Expect a research and community-driven culture that values model openness, rigorous evaluation and empowering builders to deploy capable AI without reliance on closed providers.