Product Intelligence Data Scientist

About the role

We are looking for a Product Intelligence Data Scientist to build a unified intelligence layer that turns raw usage signals, behavioural data and lifecycle events into predictive and prescriptive insights. You will own analytical models that link product behaviour to business outcomes across acquisition, activation, adoption, expansion and renewal.

Key responsibilities

• Partner with Product to define and validate hypotheses around feature adoption, friction points and growth levers using experimentation.
• Translate complex product signals into lifecycle intelligence that drives prioritisation of builds, fixes or sunsetting.
• Build and own predictive models for adoption maturity, churn risk, expansion propensity and feature‑market fit.
• Design behavioural segmentation and cohort frameworks to reveal how users derive value.
• Develop causal and inferential analyses that connect product interactions to revenue outcomes such as NRR and LTV.
• Create automated scoring systems (product‑qualified leads, customer health, engagement intensity) for GTM and Customer Success workflows.
• Build early‑warning detection models to identify at‑risk accounts from behavioural shifts.
• Communicate findings to senior leadership as clear, actionable business narratives and democratise intelligence across teams.

Required profile

• 5+ years of experience in data science or advanced analytics, preferably in a hybrid PLG/sales‑led SaaS environment.
• Strong Python and SQL skills with experience in data modelling and pipeline tools such as dbt, Airflow or Dagster.
• Deep expertise in predictive modelling, causal inference, survival analysis and experimentation methods (A/B testing, diff‑in‑diff, instrumental variables).
• Hands‑on experience with product telemetry and event‑stream data at scale.
• Solid understanding of SaaS lifecycle metrics (ARR, NRR, LTV, PQLs, activation funnels).
• Degree in a quantitative discipline (Statistics, Computer Science, Mathematics, Physics, Economics or equivalent).

Required skills

• Python
• SQL
• dbt
• Airflow
• Dagster
• Predictive modelling
• Causal inference
• Survival analysis
• A/B testing
• Diff‑in‑diff
• Instrumental variables
• Snowflake
• Databricks
• BigQuery
• Azure Synapse
• Amplitude
• Mixpanel
• Pendo
• MLOps (model deployment, monitoring, retraining)

What we offer

• Flexible benefits fund, emergency leave days, adoption leave.
• 28 days annual leave plus bank holidays, pension, life cover, private medical insurance.
• Parental leave, education assistance program.
• Hybrid working model with expectation of 50% on‑site presence.