Product Intelligence Data Scientist

Overview

In this role you will build a unified intelligence layer that turns raw product signals and lifecycle events into actionable insights. You will own models and frameworks linking product behavior to business outcomes across acquisition, activation, adoption, expansion, and renewal. You will work with cross-functional teams to surface what to build, fix, or sunset, and to drive data-informed product and commercial decisions. The opportunity emphasizes scalable analytics, predictive scoring, and early warning for at-risk accounts to influence GTM and CS actions.

Pay / Benefits

• Flexible benefits fund
• emergency leave days
• adoption leave
• 28 days annual leave (plus bank holidays)
• pension
• life cover? (life cover) – explicitly listed as life cover? yes; private medical insurance? yes; parental leave; education assistance program

Responsibilities

• Define and validate hypotheses on feature adoption and growth levers using experimentation
• Translate product signals into lifecycle intelligence to guide prioritization
• Build and own predictive models for adoption maturity, churn risk, expansion propensity, and feature-market fit
• Design behavioral segmentation and cohort frameworks to reveal value realization
• Develop causal and inferential analyses linking product interactions to revenue outcomes (NRR, LTV, expansion)
• Create automated scoring systems (product-qualified leads, customer health, engagement intensity) feeding GTM and in-product workflows
• Build early-warning detection models identifying at-risk accounts from behavioral shifts
• Communicate findings to senior leadership as actionable narratives and democratize insights for the wider team

Key requirements

• 5+ years in data science or advanced analytics in a hybrid PLG/sales-led SaaS environment
• Strong Python and SQL; experience with data modelling and pipeline tools (dbt, Airflow, Dagster)
• Deep expertise in predictive modelling, causal inference, survival analysis, and experimentation (A/B, diff-in-diff, instrumental variables)
• Experience with product telemetry and event-stream data at scale
• Solid understanding of SaaS lifecycle mechanics (ARR, NRR, LTV, PQLs, activation funnels)
• Ability to translate statistical findings into product and commercial strategy for non-technical stakeholders
• Degree in a quantitative discipline (Statistics, Computer Science, Mathematics, Physics, Economics, or equivalent)

• Strong communication with non-technical stakeholders
• Cross-functional collaboration
• Storytelling with data
• Python
• SQL
• dbt

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