Product Intelligence Data Scientist

Salary: £45,000 - 85,000 per year

Requirements:

• 5+ years in data science or advanced analytics, ideally 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 such as A/B testing, diff-in-diff, and instrumental variables
• Experience working directly with product telemetry and event-stream data at scale
• Solid understanding of SaaS lifecycle mechanics including ARR, NRR, LTV, PQLs, and activation funnels
• Ability to translate statistical findings into product and commercial strategy for non-technical stakeholders
• Degree in a quantitative discipline such as Statistics, Computer Science, Mathematics, Physics, Economics, or equivalent
• Masters or PhD in a quantitative field is desirable
• Experience with cloud data platforms such as Snowflake, Databricks, BigQuery, or Azure Synapse is desirable
• Product analytics instrumentation experience with Amplitude, Mixpanel, Pendo, or custom event pipelines is desirable
• MLOps experience, including model deployment, monitoring, and retraining in production, is desirable
• Experience building propensity or scoring models that integrate into CRM or customer success platforms is desirable
• Familiarity with industrial software or enterprise SaaS contexts is desirable

Responsibilities:

• Partner with Product to define and validate hypotheses around feature adoption, friction points, and growth levers using experimentation methods
• Translate complex product signals into lifecycle intelligence that drives prioritization, surfacing what to build, fix, or sunset
• Build and own predictive models that quantify adoption maturity, churn risk, expansion propensity, and feature-market fit from product telemetry
• Design behavioral segmentation and cohort frameworks that reveal how users derive value, and where they do not
• Develop causal and inferential analyses that connect product interactions to revenue outcomes such as NRR, LTV, and expansion
• Create automated scoring systems such as product-qualified leads, customer health, and engagement intensity that feed into GTM, Customer Success, and in-product workflows
• Build early-warning detection models that identify at-risk accounts from behavioral shifts before lagging indicators appear
• Communicate findings to senior leadership as clear, actionable business narratives, and democratise intelligence findings for the wider team

Technologies:

• Airflow
• Azure
• BigQuery
• Cloud
• CRM
• Databricks
• MLOps
• Python
• SQL
• Snowflake
• dbt
• Support

More:

We are AVEVA, a global leader in industrial software with more than 6,500 employees in over 40 countries. Our software is trusted by over 90% of leading industrial companies and is used by thousands of enterprises to deliver essential services such as energy, infrastructure, chemicals, and minerals safely, efficiently, and more sustainably. We are committed to embedding sustainability and inclusion into our operations, culture, and core business strategy. This role is based in Cambridge or London Cannon Street, with a hybrid working model and an expectation of being on-site for 50% of working hours for most roles, though the arrangement for this position will be confirmed during the hiring process. We offer a comprehensive benefits package that may include a flexible benefits fund, emergency leave days, adoption leave, 28 days annual leave plus bank holidays, pension, life cover, private medical insurance, parental leave, and an education assistance program, with specific benefits varying by country.

last updated 40 week of 2026

#J-18808-Ljbffr

Full job description from WhatJobs. Apply for this role →