Strategy Analytics Manager

About Lendable

Lendable is on a mission to build the world's best technology to help people get credit and save money. We're building one of the world’s leading fintech companies and are off to a strong start:

• 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)

So far, we’ve rebuilt the Big Three consumer finance products from scratch: loans, credit cards and car finance. We get money into our customers’ hands in minutes instead of days.

We’re growing fast, and there’s a lot more to do: we’re going after the two biggest Western markets (UK and US) where trillions worth of financial products are held by big banks with dated systems and painful processes.

Join us if you want to

1. Take ownership across a broad remit. You are trusted to make decisions that drive a material impact on the direction and success of Lendable from day 1

2. Work in small teams of exceptional people, who are relentlessly resourceful to solve problems and find smarter solutions than the status quo

3. Build the best technology in-house, using new data sources, machine learning and AI to make machines do the heavy lifting

The role

We are hiring a Strategy Analytics Manager or Senior Manager – Operations, with the final level determined by the successful candidate’s experience.

You will act as the COO’s go-to analytical partner, reporting to the CRO: combining strategic judgement, strong stakeholder management and hands-on technical delivery. This is both a management and coding role - you will lead a small team while personally delivering high-priority analysis using SQL and Python.

Key themes of the role

1. Operations MI, KPI and customer outcome metrics ownership

You will have full accountability for Operations management information and performance reporting.

You will:

• 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 where operational processes or service performance are creating customer friction, repeat contact or poor outcomes

• Ensure reporting is accurate, consistent and trusted by senior leadership

• Develop strategic north-star metrics that show whether Operations is becoming more effective and scalable

• Move the function beyond retrospective reporting towards forward-looking insight and decision support

2. Workflow optimisation and operational strategy

You will work closely with Operations Directors, Heads of Department, the Operations Transformation Office and Product teams to identify, prioritise and deliver the highest-value operational opportunities.

You will:

• Diagnose bottlenecks, failure demand, customer friction and inefficient workflows

• Work with Transformation and Product to define which problems and opportunities to pursue

• Identify the lowest-hanging fruit and quantify the potential operational and customer value

• Recommend improvements to processes, routing, tooling, products and ways of working

• Define clear hypotheses, baselines and success measures before changes are implemented

• Measure realised impact precisely and determine whether initiatives should be scaled, adjusted or stopped

• Translate analysis into clear decisions, actions and ownership

3. Demand, SLAs and resourcing

You will own the analytical cycle supporting operational planning and performance decisions.

This includes:

• Understanding changes in demand and customer contact behaviour

• Supporting forecasting, capacity and headcount decisions

• Evaluating SLA and service-level trade-offs

• Measuring throughput and productivity consistently

• Identifying emerging risks or operational pressure points

• Helping leaders make evidence-based prioritisation and resourcing decisions

You will be expected to explain not only what happened, but why it happened, what should change and how success should be measured.

4. Automation, AI and strategic measurement

You will partner closely with Data Science and Operations teams to assess the impact of automation, AI and LLM-led initiatives.

You will:

• Define hypotheses, baselines, control groups and success metrics

• Measure time saved, quality improvements, risk reduction and customer impact

• Identify unintended consequences or displacement of work

• Prioritise automation opportunities based on value and feasibility

• Ensure claimed benefits are supported by credible measurement

You will also develop an understanding of the regulatory environment surrounding fintech Operations, including complaints, vulnerability, fraud, PEP and sanctions screening, customer due diligence and conduct risk.

Technical requirements

You must be 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 judgement

• 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

Leadership and stakeholder skills

• Strong commercial and operational judgement

• 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

Team

You will initially manage:

• One Senior Analytics Engineer

• One Analytics Engineer

Over time, you may hire an additional analyst and gradually grow the team based on business need.

You 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.

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