Data Analyst, Lendable

• 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 building or maintaining data pipelines

Desirable

• We are looking for an Operations Analytics Analyst to join the Operations Analytics team
• This is a hands-on role for someone who is strong in Python and SQL, comfortable working with operational data, and excited by the opportunity to build practical analytics and automation solutions
• You will work on a mix of reporting, deep-dive analysis, automation, and product expansion projects
• The role is particularly focused on supporting growing products, including US expansion, by setting up reporting baselines, identifying operational inefficiencies, and building proof-of-concept automations that reduce cost-to-serve and improve scalability
• This is a great opportunity for someone who enjoys context-switching between analytical problem-solving, stakeholder management, and hands-on technical delivery
• 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

• Screening Call + Python Questions
• Live Technical Python Interview
• Case Study Interview
• Final culture-add interviews