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Data Scientist

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 400 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

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 400 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



About the role


We 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. 


Our teams 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 these 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

  • 1+ years of experience in a Data Science role

  • Experience using Python and SQL

  • Strong proficiency with PyData stack

  • 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

  • Interest in machine learning engineering

  • Strong SQL and interest in data engineering

Interview process

  • Initial call with TA

  • Take home task

  • Task debrief and case study interview

  • Final interviews with Exec team

Life at Lendable (check out our Glassdoor page)


> 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 chef prepares 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 looking to make Lendable the most inclusive and open workspace in London


Check out our blog!

Life at Lendable (check out our Glassdoor page)

  • 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 chef prepares 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 looking to make Lendable the most inclusive and open workspace in London

Check out our blog!

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What You Should Know About Data Scientist, Lendable

At Lendable, we're on the lookout for a passionate Data Scientist to join our innovative team! If you're eager to make an impact in a fast-growing fintech company and help us redefine consumer finance, this role is perfect for you. Lendable is one of the UK’s newest unicorns, backed by esteemed investors like Balderton Capital and Goldman Sachs, and we've been profitable since 2017. In this role, you'll be at the forefront of developing cutting-edge credit risk models and analyzing data that will shape our underwriting and pricing decisions. Our data science team employs the latest machine learning techniques, and you will have the opportunity to harness a rich data repository to build best-in-market risk models. You'll collaborate with exceptional teammates and take ownership of projects that matter, from day one. If you have a knack for using data to solve complex problems and are excited about experimenting with new ideas, we want to hear from you! Here at Lendable, we value creativity, innovation, and a friendly work culture. Plus, our flexible work policy allows you to work from home on Mondays and Fridays, while enjoying our in-house chef's delicious lunches three days a week. Join us on our mission to make consumer finance amazing, and be part of a team that truly values your contribution!

Frequently Asked Questions (FAQs) for Data Scientist Role at Lendable
What are the primary responsibilities of a Data Scientist at Lendable?

As a Data Scientist at Lendable, you will be responsible for developing proprietary behavioral models that inform underwriting and pricing decisions. You’ll analyze data to identify market trends, design experiments for product innovations, and communicate valuable insights to stakeholders. Your work will directly contribute to enhancing our risk management strategies.

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What qualifications are required for the Data Scientist position at Lendable?

To qualify for the Data Scientist role at Lendable, applicants need to have at least 1 year of experience in a Data Science position, proficiency in Python and SQL, and a strong grasp of machine learning techniques. Communication skills and the ability to work effectively in a team are also essential.

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How does the Data Science team at Lendable support innovation?

The Data Science team at Lendable plays a crucial role in fostering innovation by designing and managing experiments for products and services. You’ll analyze results rigorously and contribute to continuous improvement in underwriting quality, using the latest methodologies and data sources.

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What is the typical interview process for a Data Scientist at Lendable?

The interview process for a Data Scientist position at Lendable generally starts with an initial call with a Talent Acquisition representative, followed by a take-home task. Candidates will then participate in a case study interview, concluding with final interviews with the Executive team.

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What is the work environment like for a Data Scientist at Lendable?

Lendable offers a dynamic work environment for Data Scientists, promoting collaboration in small, exceptional teams. Employees enjoy a flexible work schedule, with the option to work from home multiple days a week, and are supported by resources such as healthy lunches prepared by an in-house chef.

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Common Interview Questions for Data Scientist
Can you explain a project where you used machine learning techniques?

When answering this question, highlight your involvement in the project, the specific machine learning techniques used, and the outcomes achieved. Discuss the data sets, algorithms, and any challenges encountered along the way.

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How do you approach data cleaning and preparation?

Share your systematic approach to data cleaning, including identifying missing values, handling outliers, and transforming variables. Discuss tools and techniques you typically use and highlight the importance of clean data for model performance.

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What are the pros and cons of various machine learning algorithms?

Talk about a few common algorithms like linear regression, decision trees, and neural networks. Explain the contexts in which each performs well, as well as their limitations, particularly in relation to the Lendable products.

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How do you communicate complex data findings to non-technical stakeholders?

Discuss how you tailor your communication style based on your audience. Explain your strategies for simplifying complex concepts using visuals and clear language, ensuring stakeholder understanding and engagement.

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What metrics do you consider for evaluating model performance?

Describe key metrics such as accuracy, precision, recall, and F1-score. Explain why these metrics are significant and how they relate to the specific goals of a Data Scientist at Lendable.

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Describe a time you had a disagreement with a team member. How did you handle it?

Share a specific situation, focusing on the importance of open communication and collaboration. Emphasize your willingness to understand different viewpoints and reach a consensus for the benefit of the project.

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What is your experience with SQL, and how do you use it in data analysis?

Elaborate on how you utilize SQL for querying data, performing joins, and aggregating results. Provide examples of how SQL has been integral in your previous work as a Data Scientist.

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What do you know about Lendable and our mission?

Research Lendable’s values, goals, and products before the interview. Show your enthusiasm by articulating how your skills align with Lendable's mission to make consumer finance faster and friendlier.

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How would you prioritize your tasks when working on multiple projects?

Discuss your methodologies for task prioritization, such as establishing deadlines, understanding business impact, and stakeholder engagement. Highlight your adaptability and organizational skills.

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What tools do you prefer for data visualization, and why?

Mention tools like Matplotlib, Seaborn, or Tableau, and discuss their strengths. Explain how you choose the appropriate tool based on the audience and complexity of the data being visualized.

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Founded in 2014, Lendable offers loans to consumers by matching them with investors. It aims to make loan applications easier and quicker by using technology to automate credit decisions and give borrowers instant, personalized interest rates. Len...

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Full-time, hybrid
DATE POSTED
January 27, 2025

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