> Markdown version of [/jobs/ext/2701065-product-intelligence-data-scientist](https://www.wearedevelopers.com/jobs/ext/2701065-product-intelligence-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Product Intelligence Data Scientist

- **Company:** OSIsoft, LLC
- **Location:** Cambridge, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, BigQuery, Software as a Service, Data Intelligence, Python (Programming Language), Mixpanel, Cloud Services, SQL Databases, Usage Analysis, Delivery Pipeline, Snowflake, Information Technology, Machine Learning Operations, Industrial Software, Azure Synapse Analytics, Databricks
- **Published:** September 4, 2026
- **Apply:** https://aveva.wd3.myworkdayjobs.com/AVEVA_careers/job/Cambridge-United-Kingdom/Product-Intelligence-Data-Scientist_R014501

## About the Role

* 5+ years in data science or advanced analytics - ideally 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 working directly 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)

Desirable

 * Master's or PhD in a quantitative field
 * Cloud 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 production
 * Experience building propensity or scoring models that integrate into CRM/CS platforms
 * Familiarity with industrial software or enterprise SaaS contexts

## Description

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., * 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 don't
 * Develop 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 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


## Related Videos

- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai)
- [Making Data Warehouses fast. A developer's story.](https://www.wearedevelopers.com/videos/302-making-data-warehouses-fast-a-developer-s-story)
- [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment)
- [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning)

## Related Articles

- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)