lendable - Data Analyst (Operations Analytics)
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Requirements
• Minimum 1 year of experience in an analytics, data, operations, or technical role. • Strong Python skills are essential, including experience with data analysis, automation, and working with structured datasets. • Strong SQL skills, with the ability to query, join, transform, and analyse large datasets. • Good understanding of basic statistics, including distributions, averages, variance, conversion rates, confidence, and trend analysis. • Basic understanding of data science principles, such as classification, prediction, model evaluation, and feature thinking. • Strong analytical problem-solving skills and the ability to move from problem definition to insight and recommendation. • Comfortable working in ambiguous, fast-paced environments where priorities can change. • Able to operate as both a hands-on analyst and a pseudo-PM when required. • Strong communication skills, with the ability to explain analysis clearly to senior stakeholders. • Comfortable context-switching across reporting, analysis, automation, stakeholder questions, and product support. • Experience with dbt or modern analytics engineering workflows. • Experience building or maintaining data pipelines. • Experience integrating with REST APIs. • Exposure to LLMs, prompt engineering, AI automation, or AI engineering workflows. • Experience building end-to-end Python automations or internal tools. • Understanding of operational workflows such as QA, fraud, disputes, AML, IVR, workforce planning, or customer support. • Experience working with product teams or supporting new product launches.
Responsibilities
• Build reporting baselines and performance dashboards for new and growing products, including US expansion. • Analyse operational workflows 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. • Create proof-of-concept automations using Python, APIs, and LLMs to reduce manual work and improve decision-making. • Support analysis across areas such as QA, disputes, AML, fraud, customer support, vulnerability, workforce planning, and service operations. • Translate ambiguous operational problems into clear analytical questions, outputs, and recommendations. • Work closely with Operations, Product, Data, and senior stakeholders to prioritise and deliver high-impact work. • Support data quality, metric definition, and reporting consistency as new products and processes scale. • Present findings clearly to both technical and non-technical stakeholders.
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