Original listing text, shown exactly as published by the company.
What You’ll Do
- 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.
What We’re Looking For
- 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.
Nice to Have
- 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.
Interview process
- Screening Call + Python Questions
- Live Technical Python Interview
- Case Study Interview
- Final culture-add interviews
Life at Lendable
- Winning team: the opportunity to scale up one of the world’s most successful fintech companies
- Flexible working: flexible approach tailored to each role. Hybrid roles require three days in-office weekly; fully remote roles include regular opportunities for in-person connection through socials and off-sites
- Socials & connection: opportunities and events to come together, socialise, and get to know each other beyond the office walls
- Health coverage: support for your physical and mental wellbeing, including private health cover
- Retirement & savings: long-term financial wellbeing through retirement savings plans
- Employee referral programme: earn a competitive bonus when you refer successful new team members
- Office meals & snacks: enjoy a fully stocked kitchen, plus complimentary lunches prepared by in-house chefs on in-office days at select locations
- Sustainable commuting: cycle-to-work and electric vehicle salary sacrifice schemes available in select locations
Please note: The availability and details of specific benefits vary by location and role. For more information, please speak to your Talent Partner.
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