Data Scientist (Product Analytics)

We're building advanced AI-driven workflow tools that supercharge investment research for private markets. Clients span most of the world's top private equity and consulting firms, with the platform now supporting hundreds of billions of dollars in annual investment activity.

The Role

As our second Data Scientist, you'll turn product usage, customer and commercial data into insights that shape the company's next stage of growth. Commercial and Product teams are currently working with gaps in their data, and you'll be the one closing them.

Example Projects

• Revenue and cashflow forecasting: build models to project usage-based revenue and cashflow using product usage data
• Feature adoption and engagement analysis: determine which features drive retention versus which add little value, breaking results down by firm type, user role and tenure to guide product prioritisation
• Churn and account health scoring: design a health-scoring model that flags at-risk accounts using signals like login frequency, feature depth and search activity, giving Customer Success an early warning system
• CRM data quality and enrichment: audit and clean CRM records, build pipelines to keep them synced with live product usage and give Sales a reliable, current view of every account
• Sales funnel analysis: map the acquisition funnel end to end, pinpoint where deals stall and identify usage patterns that predict conversion
• Executive reporting: create a weekly leadership digest covering revenue, usage, pipeline health and notable anomalies, replacing manual spreadsheet reporting with an automated system

What We're Looking For

• Strong SQL skills, comfortable moving quickly through complex and messy real-world datasets;
• Solid Python ability for data wrangling, analysis and visualisation (pandas, matplotlib, plotly or similar);
• Hands-on experience with a BI or dashboarding tool such as Metabase, Looker or Tableau;
• Familiarity with product analytics platforms like Amplitude or Mixpanel, including event-level data and funnel analysis;
• Confidence working directly with CRM systems and data;
• A clear communicator who can turn data into a compelling narrative;
• Comfortable using AI tools as part of the workflow, including AI-assisted coding;
• Self-directed, with a track record of navigating ambiguity;
• A plus if you've worked in B2B SaaS, fintech or enterprise software where usage data is complex and customer segments vary.