Operations Data Analyst, Lendable

• If you’re technical, curious, and love owning problems end-to-end, this is the role for you
• 1-3 years of hands-on experience using SQL and Python in a professional setting
• Highly numerate with strong analytical reasoning
• Ability to independently structure analysis, build prototypes, and deliver insights
• Experience communicating data-driven recommendations to non-technical business stakeholders

Desirable

• We’re building the most advanced, data-driven operating model in fintech. As part of Lendable’s Operations Science & Strategy team, you’ll sit at the intersection of analytics, data science, and AI engineering - using data, LLMs, and automation to transform how our Operations teams work at scale
• You’ll work directly with senior Operations leadership to diagnose demand drivers, uncover commercial opportunities, build KPI frameworks, and deploy automation that materially reduces our cost-to-serve while improving customer outcomes
• 1. Automation & AI Engineering
• Use Python and LLM APIs (e.g. OpenAI) to design and deploy automation that replaces manual CS/FS processes
• Build prototypes, test with real operations data, and scale the successful ones into production workflows
• Partner with PMs and engineers to operationalise AI-driven solutions across Operations
• 2. Demand Analytics & Strategy
• Analyse key operations demand drivers (contact per customer, reason drivers, seasonality, servicing behaviour)
• Identify levers to reduce avoidable demand and improve customer outcomes
• Build analytical models that drive decision-making around cost optimisation and resourcing
• 3. KPI & Funnel Intelligence
• Build clear, robust KPI frameworks and funnel views across customer journeys
• Translate data into commercial insight, operational efficiency recommendations, and productivity tracking
• Create metrics that scale across new geographies, products, and teams
• 4. Work With Senior Leadership
• Work directly with Ops Directors, Heads of CS/FS/Fraud/Complaints, and the COO on strategic initiatives
• Turn raw data into narrative-driven recommendations that are simple, actionable, and commercially grounded
• Own analytics for high-impact projects end-to-end