Data Analyst (Operations Analytics)

📋 Description

• • Build reporting baselines and performance dashboards for new and growing products, including Lendable’s US expansion, to establish clear operational metrics and tracking frameworks.
• • Analyse operational workflows across QA, disputes, AML, fraud, customer support, vulnerability, and workforce planning to identify bottlenecks, inefficiencies, and low-hanging opportunities for improvement.
• • Use Python and SQL to investigate operational performance, cost-to-serve, customer outcomes, and commercial impact through structured data analysis and transformation.
• • Create proof-of-concept automations using Python, APIs, and LLMs to reduce manual work, improve decision-making, and increase scalability of operational processes.
• • Translate ambiguous operational problems into clear analytical questions, actionable insights, and data-driven recommendations for cross-functional teams.
• • Collaborate closely with Operations, Product, Data, and senior stakeholders to prioritise, scope, and deliver high-impact analytical projects.
• • Support data quality initiatives, metric definition, and reporting consistency as new products and processes scale across markets.
• • Present analytical findings clearly and compellingly to both technical and non-technical stakeholders, ensuring alignment on key operational priorities.
• • Work in a fast-paced, ambiguous environment where priorities shift rapidly, requiring agility and strong problem-solving skills.
• • Operate as both a hands-on analyst and a pseudo-product manager, balancing technical delivery with stakeholder management and project coordination.
• • Contribute to the automation of repetitive tasks in service operations, including integrating with internal and external systems via APIs.
• • Support the expansion of Lendable’s operations into the US market by establishing foundational analytics, monitoring systems, and scalability frameworks from day one.
• • Engage with diverse operational domains such as IVR, customer support workflows, fraud detection, and workforce planning to uncover insights that reduce cost-to-serve and improve customer outcomes.
• • Maintain and evolve analytical outputs as product lines grow, ensuring metrics remain accurate, relevant, and aligned with business objectives.
• • Participate in the end-to-end delivery of analytics projects—from problem scoping and data extraction to automation implementation and stakeholder communication.