Responsibilities

• Find the bets. Map our funnels, spot the constraints, and propose where to focus across acquisition, activation, retention and monetisation.
• Form sharp hypotheses. Each one with a clear mechanism, target segment, expected impact and success metric.
• Build and ship the test. Use AI tools (e.g. Claude Code, Codex, Cursor, v0, Lovable, etc.) plus no-code (Webflow, Make, Retool, Zapier) to produce working prototypes, landing pages, onboarding flows, email sequences, internal tools, or whatever the experiment needs. Wire in the instrumentation to measure it.
• Read the results honestly. Set up the tracking, run the analysis, document the learning — even when the answer is "this didn't work."
• Double down on what scales. When something works, let’s do more of it and productionalise it so that we can reap ROI and grow revenues and profitability
• Build the muscle. Maintain a public experiment backlog and reusable playbooks so the whole company gets sharper over time.
• Recent Examples Of What This Looks Like
• - An automated lead-scoring brain that surfaces the highest-intent SMEs for the Kriya-Allica team to work actively, so sales effort goes where it converts.
• - AI-powered initial customer contact that auto-generates indicative quotes and collects supporting documents, compressing days of back-and-forth into minutes.
• - AI credit underwriting agents that risk-assess applications and accelerate the path from application to decision.
• - CRM reactivation campaigns that keep Kriya-Allica front of mind for the tens of thousands of companies already in our database.

Requirements

• 3+ years in growth, product, analytics or operator roles where you owned outcomes, not just outputs.
• A track record of designing and running experiments that moved revenue, CAC, LTV or conversion — and the analytical confidence to interpret them properly (funnels, cohorts, basic stats, knowing when a result is real).
• Hands-on fluency with AI tools for building, not just writing. You can go from idea to clickable prototype to instrumented test without waiting on engineering.
• Comfort stitching together no-code, APIs, webhooks and lightweight backends to make experiments work end-to-end.
• High ownership and a strong commercial instinct — you connect every test back to revenue, positioning or unit economics.

Preferred

Benefits

• Full onboarding support and continued development opportunities
• Options for flexible working
• Regular social activities
• Pension contributions
• Discretionary bonus scheme
• Private health cover
• Life assurance
• Family friendly policies including enhanced Maternity & Paternity leave