Top Tech Jobs & Startup Jobs in London

Build and maintain scalable data platform solutions supporting analytics and business decision-making. Collaborate with product, analytics, and engineering teams to solve complex problems, improve data reliability, manage infrastructure, and enable fast insights. Responsibilities include developing data-intensive systems, working with cloud platforms and warehouses, applying infrastructure-as-code and CI/CD practices, and balancing scalability, cost, security, and delivery speed.

Build and improve production AI systems, including chatbots, autonomous account actions, document extraction pipelines, and LLM-driven case-management workflows. The role covers discovery, prototyping, deployment, evaluation, monitoring, prompt design, retrieval, agent workflows, and identifying manual processes suitable for automation.

Analyze pricing, loan funnel, and credit optimization opportunities within Lendable’s UK Loans team. Responsibilities include researching pricing strategy improvements, developing analytical models, collaborating with credit and product teams, working with large datasets, communicating findings to stakeholders, and gradually taking ownership of models. The role requires Python, SQL, statistical and machine learning knowledge, strong numerical ability, and effective teamwork.

Analyze pricing, funnel performance, and new product development for the UK Loans team. The role involves framing ambiguous business problems, defining metrics, conducting SQL-based analyses, evaluating pricing strategies, researching improvements, and communicating recommendations to stakeholders. The analyst will collaborate with credit and product teams, develop commercial and analytical expertise, and progressively take ownership of strategic analyses and initiatives.

Manage partner relationships and execute sponsorship activations at Lendable. Drive measurable growth through partnerships and optimize performance metrics.

Build and maintain full-stack web applications and automations for Central Operations using Python, FastAPI, React, and Next.js. Deliver tools used by operations teams, improve workflows through intelligent automation, and collaborate with Product, Data Science, and Analytics. Contribute to technical direction, apply strong engineering practices, and explore generative AI and LLM solutions to improve customer support, fraud prevention, complaints processing, and operational efficiency.

Build and maintain internally facing web applications and automations for Operations teams. Develop full-stack features using Python, FastAPI, JavaScript or TypeScript, React, and Next.js. Collaborate with Product, Data Science, and Analytics teams to deliver scalable operational tools and pragmatic AI solutions, including LLM-powered workflows. Take ownership of technical decisions, testing, code quality, system design, and feature delivery in a product-focused environment.

Build and improve Lendable’s data platform by delivering scalable, reliable, secure, and cost-effective data solutions. Partner with product, analytics, and engineering teams to enable fast analytics and insight generation. Responsibilities include developing data-intensive systems, applying modern data architectures, managing cloud infrastructure and infrastructure-as-code, supporting CI/CD, evaluating build-versus-buy decisions, and improving platform operations while working autonomously in an ambiguous environment.

Owns high-volume, end-to-end recruitment across Operations, including Complaints, Fraud, Financial Crime, and Customer Operations. The role manages a small team of Talent Partners while recruiting individually, partnering with leaders, using data and AI to improve hiring processes, developing sourcing strategies, forecasting pipeline needs, and optimizing candidate conversion in a fast-paced scale-up environment.

Build and improve analytical data models supporting lending decisions, pricing, portfolio analysis, and investor reporting. Develop scalable ELT transformations, strengthen data quality and reliability, support analysts, troubleshoot pipeline issues, and improve transformation efficiency and cost-effectiveness. Collaborate with product, engineering, analytics, and business stakeholders while promoting analytics engineering standards and effective use of AI tools.