Product Analyst

Job Description We are looking for a Product Analyst focused on customer adoption, usage, impact, and sentiment to help our teams understand how customers experience SmartRecruiters in the real world – and how that translates into measurable value. In this role, you will work closely with some of our largest customers and internal stakeholders (Product, Product Marketing, Customer Success, Sales, and Data teams) to design metrics, build analytical views, and deliver clear, compelling insights on: Adoption of key features and workflows Usage depth and quality across personas and segments Business impact (efficiency, time saved, hiring outcomes, ROI) Sentiment and satisfaction across feedback channels You’ll combine strong product analytics skills with AI-backed analysis to move quickly from raw data to decisions, and turn complex signals into simple, customer-ready narratives. What You’ll Deliver Customer-facing product insights & value stories Partner with Customer Success, Sales, and Product to build analyses that show how customers are adopting and using key capabilities (e.g., automation, AI features, messaging, reporting) and what outcomes they are achieving. Create reusable analytical frameworks and templates that can be applied across customers (e.g., time saved, recruiter capacity unlocked, improvements in time-to-hire, conversion rates). Adoption, usage, impact, and sentiment frameworks Define and refine hero metrics and supporting KPIs for core product areas, with a specific focus on adoption, usage quality, customer impact, and sentiment. Design metric definitions and documentation that are easy to understand and reuse across Product, GTM, and Exec audiences. Deep dives and decision support Lead hypothesis-driven deep dives on product performance (e.g., feature launches, AI capabilities, automation workflows), combining quantitative data with qualitative signals to explain what’s happening and why. Translate findings into clear recommendations and influence product and go-to-market decisions. Instrumentation and tracking quality Collaborate with Product Managers and Engineers to ensure we have the right event tracking and data structures to measure adoption, usage, impact, and sentiment. Review tracking proposals, validate events in test environments, and raise/track data quality issues where needed. Dashboards, tools, and self-serve views Build and maintain robust dashboards and reports that make it easy for stakeholders to monitor adoption, usage, impact, and sentiment across customers and cohorts. Work with Product and CS to ensure these assets are understood, trusted, and actively used. AI-backed analysis and workflows Use AI tools and copilots to explore large datasets, generate hypotheses, summarise patterns, and accelerate routine analytical tasks. Experiment with LLM-based approaches (e.g., natural-language exploration of metrics, automated insight generation, clustering of feedback and sentiment) and help define best practices for AI-assisted product analytics. Storytelling and enablement Turn complex analyses into concise, compelling narratives for QBRs, product updates, customer-facing decks, and internal decision forums. Enable colleagues (PMs, CSMs, Sales, Product Ops) to reuse your work through clear documentation and playbooks. Qualifications Qualifications – Required Experience & domain 3+ years in Product Analytics, Data Analytics, or a similar role within a B2B SaaS or product-led organisation. Demonstrated experience analysing adoption, usage, and impact of product features; comfortable linking product behaviour to business outcomes (e.g., efficiency, time-to-hire, conversion, ROI). Data & tooling Strong SQL skills and experience working with large datasets in modern warehouses (e.g., BigQuery, Snowflake, Redshift). Hands-on experience with BI / visualisation tools (e.g., Looker/Looker Studio, Tableau, Power BI) to build dashboards and reports. Experience with event tracking and product analytics concepts (event schemas, taxonomies, funnels, cohorts, retention). AI & AI-backed analysis Practical experience using AI tools (e.g., ChatGPT, Gemini, Cursor, or similar) to accelerate analysis – from query generation and code review to summarising findings and exploring hypotheses. Ability to design effective prompts and workflows that combine LLMs with SQL/BI tools to speed up discovery while maintaining rigour. Comfortable evaluating when AI-generated insights are directionally useful vs. when deeper manual/statistical analysis is required. Analytical & statistical skills Strong quantitative reasoning; able to structure problems, choose appropriate methods, and interpret results carefully. Familiarity with A/B testing, experimentation, or causal inference concepts (you know the difference between correlation and causation and when it matters). Collaboration & communication Excellent communication skills; able to translate complex data into clear, concise stories tailored to Product,