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About the Role

Lendable is seeking a Strategy Analytics Manager or Senior Manager – Operations to partner with the COO, combining strategic judgment, stakeholder management, and technical delivery. This role involves leading a small team and performing high-priority analysis using SQL and Python.

Responsibilities

• Own MI across front-office and back-office Operations departments.
• Define and govern KPIs covering demand, SLAs, throughput, productivity, quality, and customer outcomes.
• Ensure operational efficiency is balanced with fair, timely, and effective outcomes for customers.
• Identify operational processes or service performance issues causing customer friction, repeat contact, or poor outcomes.
• Ensure reporting is accurate, consistent, and trusted by senior leadership.
• Develop strategic north-star metrics for Operations effectiveness and scalability.
• Transition the function from retrospective reporting to forward-looking insight and decision support.
• Work with Operations Directors, Heads of Department, the Operations Transformation Office, and Product teams to identify, prioritize, and deliver high-value operational opportunities.
• Diagnose bottlenecks, failure demand, customer friction, and inefficient workflows.
• Collaborate with Transformation and Product teams to define problems and opportunities.
• Identify low-hanging fruit and quantify potential operational and customer value.
• Recommend improvements to processes, routing, tooling, products, and ways of working.
• Define clear hypotheses, baselines, and success measures before implementing changes.
• Precisely measure realized impact and determine initiative scaling, adjustment, or termination.
• Translate analysis into clear decisions, actions, and ownership.
• Own the analytical cycle supporting operational planning and performance decisions.
• Understand changes in demand and customer contact behavior.
• Support forecasting, capacity, and headcount decisions.
• Evaluate SLA and service-level trade-offs.
• Measure throughput and productivity consistently.
• Identify emerging risks or operational pressure points.
• Assist leaders in making evidence-based prioritization and resourcing decisions.
• Explain not only what happened, but why it happened, what should change, and how success should be measured.
• Partner with Data Science and Operations teams to assess the impact of automation, AI, and LLM-led initiatives.
• Define hypotheses, baselines, control groups, and success metrics for automation initiatives.
• Measure time saved, quality improvements, risk reduction, and customer impact from automation.
• Identify unintended consequences or displacement of work from automation.
• Prioritize automation opportunities based on value and feasibility.
• Ensure claimed benefits of automation are supported by credible measurement.
• Develop an understanding of the regulatory environment in fintech Operations, including complaints, vulnerability, fraud, PEP and sanctions screening, customer due diligence, and conduct risk.

Requirements

• Highly hands-on and comfortable working directly with data.
• Very strong SQL and Python.
• Experience working with APIs.
• Advanced Excel and strong 80/20 analytical judgment.
• Understanding of semantic data models and good analytics engineering practices.
• Basic statistics, experimentation, and causal measurement knowledge.
• Understanding of Data Science, automation, and LLM principles.
• dbt experience is helpful but not essential.
• Strong commercial and operational judgment.
• High emotional intelligence and stakeholder management skills.
• Comfortable influencing and constructively challenging senior leaders.
• Able to translate complex analysis into simple business decisions.
• Capable of working at pace across several competing priorities.
• Will initially manage: One Senior Analytics Engineer, One Analytics Engineer.
• Over time, may hire an additional analyst and gradually grow the team based on business need.
• Will set the direction and priorities of the Operations analytics function, develop the team, and create an effective operating model across Analytics, Analytics Engineering, Data Science, and Operations.

Skills

• SQL
• Python
• APIs
• Excel
• 80/20 analytical judgement
• Semantic data models
• Analytics engineering practices
• Basic statistics
• Experimentation
• Causal measurement
• Data Science principles
• Automation principles
• LLM principles
• dbt
• Commercial judgment
• Operational judgment
• Emotional intelligence
• Stakeholder management
• Influencing
• Challenging senior leaders
• Translating complex analysis
• Working at pace
• Competing priorities

Location

• Hybrid
• Remote

Work Type

• Hybrid
• Remote
• Full-time

Experience Level

• Manager
• Senior Manager

Benefits

• Flexible working
• Private health cover
• Retirement savings plans
• Employee referral programme
• Office meals & snacks
• Cycle-to-work schemes
• Electric vehicle salary sacrifice schemes

About the Company

• Lendable is on a mission to build the world's best technology to help people get credit and save money.
• Building one of the world’s leading fintech companies.
• One of the UK’s newest unicorns with a team of just over 700 people.
• Among the fastest-growing tech companies in the UK.
• Profitable since 2017.
• Backed by top investors including Balderton Capital and Goldman Sachs.
• Loved by customers with the best reviews in the market (4.9 across 10,000s of reviews on Trustpilot).
• Rebuilt the Big Three consumer finance products from scratch: loans, credit cards and car finance.
• Get money into customers’ hands in minutes instead of days.
• Growing fast and expanding into the two biggest Western markets (UK and US).
• Targeting markets where trillions worth of financial products are held by big banks with dated systems and painful processes.