Senior Data Analyst (Marketing)

Lendable
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

About Lendable

Lendable is on a mission to make consumer finance amazing: faster, cheaper, and friendlier. 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 600 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

About the Role

We are looking for an experienced Senior Data Analyst to run marketing and growth analytics in our rapidly evolving multi‑product app. As Lendable scales its products across lending, engagement features and other financial services, the complexity and volume of data‑driven decisions have grown exponentially. This role is central to unlocking insights that will shape our acquisition efficiency and fuel product innovation, overseeing the full breadth of digital and non‑digital channels across multiple products.

You will be the senior analytical voice across our Zable direct marketing initiatives – owning end‑to‑end marketing funnel data, capture requirements, insight generation, experimentation strategy, and advanced analytics projects. Beyond individual contribution, you will instill best practices and be a key thought partner to product, growth, and engineering leaders.

What You’ll DoImpact & Analytical Leadership
  • Analyse marketing campaign performance across multiple channels and conduct analyses to identify trends and opportunities for optimisation

  • Structure and execute high‑impact analyses in acquisition funnels to inform marketing spend and product evolution

  • Drive experimentation rigor: design, implement, and analyse A/B tests, multivariate experiments, and cohort studies

  • Co‑operate with product and credit teams to ensure mid to deep funnel performance is improved.

Data Product Involvement
  • Influence data architecture by contributing to schema design that enable more insights

  • Build high quality, scalable pipelines and transformations that increase interpretability of data and increase the team’s agility

Cross‑Functional Partnership
  • Serve as a thought partner to Product Managers and Credit Managers – ensuring analytical insight drives roadmap prioritisation and credit assessments.

  • Collaborate with the creative and design team to deepen insights at the creative and campaign level to enhance prioritisation of their ideas.

What We’re Looking ForCore Qualifications
  • 3+ years of experience in data analytics, ideally within a product‑led or high‑growth tech environment

  • Deep expertise in analytical methods including funnel optimisation, segmentation, cohort analysis, LTV modelling, and regression techniques.

  • High proficiency in SQL and strong command of one or more analytics tools or statistical packages (e.g., Python, R).

  • Hands‑on experience with DBT and familiarity with data modelling best practices

  • Skilled in experimentation frameworks (A/B testing, hypothesis testing, causal inference).

  • Demonstrated ability to turn ambiguous problems into structured, hypothesis‑driven, data‑supported insights.

  • Excellent communication skills with experience influencing cross‑functional stakeholders and product leaders.

Nice to Have
  • Experience in credit marketing (credit cards, loans, car finance etc.)

  • Experience with paid media platforms (Google Ads, Meta, TikTok etc.) and working with marketing teams

  • Experience building or contributing to Marketing Mix Models or incrementality testing frameworks.

  • Familiarity with data instrumentation and schema design in product development cycles

Interview process:

  • Screening call

  • Take home task and cognitive tests

  • Case study

  • Final Interview

Life at Lendable
  • 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, those based in the UK come together IRL at our Shoreditch office in London to be together, build and exchange ideas.

  • Enjoy a fully stocked kitchen with everything you need to whip up breakfast, lunch, snacks and drinks 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 looking to make Lendable the most inclusive and open workspace in London

Check out our blog!


#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.

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.

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.

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.