Jobs at Help Scout

Product Marketing Manager responsible for positioning, storytelling, launches, enablement, competitive research, and cross-functional collaboration to drive awareness and adoption of Help Scout's B2B SaaS product.

Produce polished, on-brand visual work across static, motion, and web channels. Design ads, social, landing and product pages, collaborate with product marketing, balance fast turnarounds and larger brand projects, and grow into shaping the brand system.

Own the vision, roadmap, and execution for Help Scout’s autonomous AI "Agent" pillar (Agent Drafts, AI Answers, Agentic Actions, Docs automation). Lead consolidation onto a single backend, define trust-building UX and outcome-based monetization, partner with engineering, Intelligence and Conversations PMs, GTM and CS, and spend time with customers to drive adoption and product decisions.

Lead the Intelligence product pillar, owning roadmap and delivery for Topics, Sentiment, CX Score, and profiles. Ship early value, define monetization and conversion, coordinate cross-pillar interfaces, partner with data and gateway teams, engage customers, and work with GTM on messaging for a net-new data product in Help Scout's Understand tier.

Build end-to-end, customer-facing AI features with a frontend focus: implement LLM-backed flows (RAG, embeddings, agentic tool-calling), craft fast accessible UIs in TypeScript/React, instrument evaluation/guardrails and production monitoring, stay close to customers, and own outcomes from design through production.

Own Mixpanel and partner closely with Product to turn product-level data into decision-grade insights. Define instrumentation and event taxonomy, build dashboards and notebooks, run cohort and retention analyses, design and analyze A/B tests, and collaborate with data engineers to model product event data. Document metrics, maintain a living glossary, and translate findings into actionable product recommendations.

Build customer-facing intelligence features end-to-end with a frontend focus using TypeScript and React. Ship production-grade LLM capabilities (RAG, embeddings, prompt engineering), ensure reliability, observability, accessibility, and stay close to customers to measure outcomes.

Build end-to-end, customer-facing AI features with a frontend center of gravity: implement LLM-backed flows (RAG, embeddings, agentic tool-calling), craft fast accessible UIs in TypeScript/React, instrument evaluation and monitoring, and own outcomes from design through production and customer feedback.