Data Scientist

Lendable Ltd
London
4 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist (NLP & LLM Specialist)

About the roleWe are excited to be hiring a new Data Scientist into our team! Lendable is the market leader in real rate risk-based pricing, offering consumers transparency and product assurance at the point of application. Data Science sits at the heart of this USP, developing the credit risk models to underwrite loan and credit card products.You will have access to the latest machine learning techniques combined with a rich data repository to deliver best in market risk models.This role will primarily focus on our US unsecured loans and credit cards business.Our team’s objectives* The data science team develops proprietary behavioural models combining state of the art techniques with a variety of data sources that inform market-facing underwriting and pricing decisions, scorecard development, and risk management.* Data scientists work across the business in a multidisciplinary capacity to identify issues, translate business problems into data questions, analyse and propose solutions.* We self-serve with all deployment and monitoring, without a separate machine learning engineering team.* Design, implement, manage and evaluate experiments of products and services leading to constant innovation and improvement.How you’ll impact those objectives* Learn the domain of products that Lendable serves, understanding the data that informs strategy and risk modelling is essential to being able to successfully contribute value.* Rigorously search for the best models that enhance underwriting quality.* Clearly communicate results to stakeholders through verbal and written communication.* Share ideas with the wider team, learn from and contribute to the body of knowledge.* Key Skills* Experience using Python and SQL.* Strong proficiency with data manipulation including packages like NumPy, Pandas.* Knowledge of machine learning techniques and their respective pros and cons.* Confident communicator and contributes effectively within a team environment.* Self driven and willing to lead on projects / new initiatives.Nice to have* Prior experience of credit risk for consumer lending or credit cards, especially for the US market.* Interest in machine learning engineering.* Strong SQL and interest in data engineering.The interview process* Initial call with TA* Take home task* Task debrief and case study interview* Final interviews with leadership team* 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.