Data Analyst (Operations Science & Strategy)

Lendable Ltd
London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

About the roleWe’re building the most advanced, data-driven operating model in fintech. As part of Lendable’s Operations Science & Strategy team, you’ll sit at the intersection of analytics, data science, and AI engineering - using data, LLMs, and automation to transform how our Operations teams work at scale.You’ll work directly with senior operations leadership working on special projects to diagnose demand drivers, uncover commercial opportunities, build KPI frameworks, and deploy automation that materially reduces our cost-to-serve while improving customer outcomes.If you’re technical, curious, and love owning problems end-to-end, this is the role for you.## What You’ll Do### 1. Automation & AI Engineering* Use Python and LLM APIs (e.g. OpenAI) to design and deploy automation that replaces manual CS/FS processes.* Build prototypes, test with real operations data, and scale the successful ones into production workflows.* Partner with PMs and engineers to operationalise AI-driven solutions across Operations.### 2. Demand Analytics & Strategy* Analyse key operations demand drivers (contact per customer, reason drivers, seasonality, servicing behaviour).* Identify levers to reduce avoidable demand and improve customer outcomes.* Build analytical models that drive decision-making around cost optimisation and resourcing.### 3. KPI & Funnel Intelligence* Build clear, robust KPI frameworks and funnel views across customer journeys.* Translate data into commercial insight, operational efficiency recommendations, and productivity tracking.* Create metrics that scale across new geographies, products, and teams.### 4. Work With Senior Leadership* Work directly with Ops Directors, Heads of CS/FS/Fraud/Complaints, and the COO on strategic initiatives.* Turn raw data into narrative-driven recommendations that are simple, actionable, and commercially grounded.* Own analytics for high-impact projects end-to-end.## What We’re Looking For### Must-Have Skills* 1-3 years of hands-on experience using SQL and Python in a professional setting.* Highly numerate with strong analytical reasoning* Ability to independently structure analysis, build prototypes, and deliver insights.* Experience communicating data-driven recommendations to non-technical business stakeholders.### Nice-to-Haves* Experience with dbt or modern data stacks.* Familiarity with LLMs, APIs, or interest in building AI automation.* Exposure to product, strategy, or operations environments.* Thrives in fast-paced, ambiguous environments.* Comfortable wearing multiple hats Analyst, Data Scientist, AI Engineer, PM.* Highly motivated, entrepreneurial mindset, and excited by building the strongest operating model in fintech.## Why Join* Work on high-visibility, high-impact projects that directly shape the future of Lendable’s operations.* Build automation and analytics used daily by hundreds of agents and thousands of customers.* Collaborate with a small, sharp, and ambitious team that moves fast.* Real ownership: if you build it, you’ll see it shipped and scaled.* The opportunity to scale up one of the world’s most successful fintech companies* Best-in-class compensation, including equity* You can work from home every Monday and Friday if you wish - on the other days we all come together IRL to be together, build and exchange ideas* Our in-house chefs prepare fresh, healthy lunches in the office every Tuesday-Thursday* We care for our Lendies’ well-being both physically and mentally, so we offer coverage when it comes to private health insurance* We're an equal opportunity employer and are keen to make Lendable the most inclusive and open workspace in London

#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.