Product Intelligence Data Scientist in Kennington

AVEVA is creating software trusted by over 90% of leading industrial companies.Job Title: Product Intelligence Data ScientistLocation: Cambridge/London, United Kingdom (Hybrid)Employment Type: full-time The Job We are looking for a Product Intelligence Data Scientist to build a unified intelligence layer that transforms raw usage signals, behavioral data, and lifecycle events into predictive, prescriptive insights — enabling teams to act on what the product is telling us, not just what gets reported.You will own the analytical models and frameworks that connect product behavior to business outcomes across acquisition, activation, adoption, expansion, and renewal.Key ResponsibilitiesPartner with Product to define and validate hypotheses around feature adoption, friction points, and growth levers using experimentation methodsTranslate complex product signals into lifecycle intelligence that drives prioritization — surfacing what to build, fix, or sunsetBuild and own predictive models that quantify adoption maturity, churn risk, expansion propensity, and feature-market fit from product telemetryDesign behavioral segmentation and cohort frameworks that reveal how users derive value — and where they don’tDevelop causal and inferential analyses that connect product interactions to revenue outcomes (NRR, LTV, expansion)Create automated scoring systems (product-qualified leads, customer health, engagement intensity) that feed into GTM, Customer Success, and in-product workflowsBuild early-warning detection models that identify at-risk accounts from behavioral shifts before lagging indicators appearCommunicate findings to senior leadership as clear, actionable business narratives, and democratise intelligence findings for the wider teamEssential Requirements5+ years in data science or advanced analytics — ideally in a hybrid PLG/sales-led SaaS environmentStrong 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 working directly with product telemetry and event-stream data at scaleSolid understanding of SaaS lifecycle mechanics (ARR, NRR, LTV, PQLs, activation funnels)Ability to translate statistical findings into product and commercial strategy for non-technical stakeholdersDegree in a quantitative discipline (Statistics, Computer Science, Mathematics, Physics, Economics, or equivalent)Desired Skills and CompetenciesMaster’s or PhD in a quantitative fieldCloud data platforms (Snowflake, Databricks, BigQuery, or Azure Synapse)Product analytics instrumentation experience (Amplitude, Mixpanel, Pendo, or custom event pipelines)MLOps — model deployment, monitoring, and retraining in productionExperience building propensity or scoring models that integrate into CRM/CS platformsFamiliarity with industrial software or enterprise SaaS contextsUK Benefits include:Flexible benefits fund, emergency leave days, adoption leave, 28 days annual leave (plus