Data Analyst (Strategic Projects)
Role Overview
The role sits in Lendable's Operations Science & Strategy team at the intersection of analytics, data science, and AI engineering.
The analyst will use data, large language models, and automation to transform operations at scale.
The work includes special projects with senior operations leadership to diagnose demand, identify commercial opportunities, build KPI frameworks, and deploy automation.
Key Skills for This Role
Full Job Posting
About Lendable
Lendable is a fintech company building technology for consumer credit and savings products, including loans, credit cards, and car finance.
The company describes itself as a fast-growing, profitable technology business backed by investors including Balderton Capital and Goldman Sachs.
Role overview
The role sits in Lendable's Operations Science & Strategy team at the intersection of analytics, data science, and AI engineering.
The analyst will use data, large language models, and automation to transform operations at scale.
The work includes special projects with senior operations leadership to diagnose demand, identify commercial opportunities, build KPI frameworks, and deploy automation.
Responsibilities
- Use Python and LLM APIs to design and deploy automation for manual customer service and financial services processes.
- Build and test prototypes with operations data and scale successful solutions into production workflows.
- Partner with product managers and engineers to operationalise AI-driven solutions.
- Analyse operational demand drivers and identify ways to reduce avoidable demand and improve customer outcomes.
- Build models for cost optimisation and resourcing decisions, plus KPI frameworks and customer-journey funnel views.
- Translate data into commercial insight, efficiency recommendations, and productivity tracking; create metrics that scale across geographies, products, and teams.
- Work directly with senior operations leaders on strategic initiatives and own high-impact project analytics end to end.
- Turn raw data into actionable, commercially grounded recommendations.
Requirements
- 2–3 years of hands-on professional Python experience.
- Strong numeracy and analytical reasoning; ability to structure analysis, build prototypes, and deliver insights independently.
- Experience communicating data-driven recommendations to non-technical business stakeholders.
- A STEM degree is a nice-to-have.
- Experience with dbt or modern data stacks is a nice-to-have.
- Familiarity with LLMs or APIs, or interest in AI automation, is a nice-to-have; exposure to product, strategy, or operations environments is also a nice-to-have.
Interview process
- The interview process includes a screening call with Python questions, a live technical Python interview, and final interviews with two senior leaders.
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