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, GTM, and customer audiences.
• Proven experience working cross-functionally with Product, Engineering, and customer-facing teams.
• Comfortable working directly with customers to understand context, present findings, and refine analyses.

Nice to Have

• Experience in HRTech / Talent Acquisition or adjacent domains.
• Hands-on experience with Python or R for deeper analysis, preferably in combination with AI coding assistants.
• Experience analysing sentiment and qualitative feedback (e.g., surveys, tickets, call notes) and combining it with quantitative product usage data.
• Experience building benchmarking or health scoring views across a customer base.

Additional Information

SmartRecruiters is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. Your information will be kept confidential according to EEO guidelines.