Strategy Analytics Manager
- Role
- Data Science
- Employment
- Full-time
- Company size
- Enterprise
Open to GB only. Set where you work from to check your eligibility.
No BS summary
Analytics manager/senior manager for fintech operations in the UK. Must be hands-on with SQL, Python, APIs, advanced Excel, statistics/experimentation, analytics engineering concepts, and able to manage a small analytics team.
Core skills
Required skills
Optional skills
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
- 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
- Work in small teams of exceptional people, who are relentlessly resourceful to solve problems and find smarter solutions than the status quo
- 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
- 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.
Check out our blog!
What you'll do
- Act as the COO’s go-to analytical partner while reporting to the CRO.
- Combine strategic judgement, stakeholder management and hands-on technical delivery.
- Lead a small team while personally delivering high-priority analysis using SQL and Python.
- Own management information 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 create customer friction, repeat contact or poor outcomes.
- Ensure reporting is accurate, consistent and trusted by senior leadership.
- Develop strategic north-star metrics showing whether Operations is becoming more effective and scalable.
- Move the function beyond retrospective reporting toward forward-looking insight and decision support.
- Work with Operations Directors, Heads of Department, the Operations Transformation Office and Product teams to identify, prioritise and deliver high-value operational opportunities.
- Diagnose bottlenecks, failure demand, customer friction and inefficient workflows.
- Work with Transformation and Product to define which problems and opportunities to pursue.
- 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 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.
- Own the analytical cycle supporting operational planning and performance decisions.
- Understand changes in demand and customer contact behaviour.
- 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.
- Help leaders make evidence-based prioritisation and resourcing decisions.
- Explain what happened, 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, AI and LLM-led initiatives.
- 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.
- Develop an understanding of the regulatory environment surrounding fintech Operations, including complaints, vulnerability, fraud, PEP and sanctions screening, customer due diligence and conduct risk.
- Initially manage one Senior Analytics Engineer and one Analytics Engineer.
- Potentially hire an additional analyst and grow the team based on business need.
- 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.
What they require
- Successful candidate’s experience will determine whether the final level is Manager or Senior Manager.
- 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.
- 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.
Benefits
- Opportunity to scale up one of the world’s most successful fintech companies.
- Flexible approach to working tailored to each role.
- Fully remote roles include regular opportunities for in-person connection through socials and off-sites.
- Opportunities and events to come together, socialise and get to know each other beyond the office walls.
- Support for physical and mental wellbeing, including private health cover.
- Retirement savings plans.
- Employee referral programme with a competitive bonus for successful referrals.
- Fully stocked kitchen.
- Complimentary lunches prepared by in-house chefs on in-office days at select locations.
- Cycle-to-work schemes available in select locations.
- Electric vehicle salary sacrifice schemes available in select locations.
Lendable is building technology to help people get credit and save money. It is a UK fintech unicorn with just over 700 people, profitable since 2017, backed by investors including Balderton Capital and Goldman Sachs, and offers loans, credit cards and car finance.