Data Scientist – Commercial Analytics (Bookings & ARR)
£45,000 + Benefits | Hybrid (1 day/week in Reading office - Flexible)
Location: London/Reading
Sector: Automotive SaaS | Big Data | Sales Analytics
Join one of the world’s leading automotive SaaS providers as they tackle large-scale data challenges across 25+ countries. Keyloop is hiring a Data Scientist to help make sense of fragmented commercial data, with a focus on sales performance, pipeline analytics, and recurring revenue (ARR) reporting.
You’ll be embedded in the team supporting the Sales function, working closely with Finance, IT, and Ops to improve data quality, stability, and decision-making across the business.
🔍 What you’ll be working on:
* Investigating, reconciling, and cleaning complex datasets across Salesforce, NetSuite, and legacy platforms
* Building Python-based tools to identify root causes of reporting discrepancies
* Supporting Bookings & ARR forecasting models, visualising pipeline trends and sales performance
* Handling large-scale monthly data validation, error reporting, and reconciliation processes
* Creating automated, scalable solutions for manual data imports and fragmented system inputs
* Collaborating across Sales, Finance, and IT to stabilise infrastructure and improve analytics capability
✅ What we’re looking for:
* Strong programming skills in Python and SQL, with experience handling large or messy datasets
* Comfortable working across fragmented data systems (e.g. Salesforce, ERP, CRMs)
* Experience in forecasting, sales analytics, or financial performance reporting
* Strong communication skills with the ability to collaborate with non-technical stakeholders
* Bonus: familiarity with Power BI, Tableau, or any experience in SaaS / subscription analytics
💡 The setup:
* Hybrid: 1 day per week in their Reading office - Flexible
* Work across 25+ global markets with the chance to impact key commercial decisions
* You’ll be part of a collaborative data-driven team helping bring stability and clarity to a rapidly scaling tech business