About the role
We are excited to be hiring a new Machine Learning Engineer 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. Machine learning sits at the heart of this USP, developing and deploying credit risk models to underwrite loan and credit card products.

Tech stack

1. Python
2. FastAPI
3. Pydantic
4. Pytorch
5. Tensorflow
6. AWS
7. Docker
8. Rabbit MQ
9. Kubernetes

Your team's objectives

1. 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.
2. This role has a focus on deploying our ML models and developing the supporting infrastructure.
3. We work across the business in a multidisciplinary capacity to identify issues, translate business problems into data questions, and architect and build solutions.

How you'll impact those objectives

1. Learning the domain of products that Lendable serves, and understanding the data that informs strategy and risk modelling is essential to being able to successfully contribute value.
2. Design systems to integrate machine learning models into scalable applications.
3. Develop and maintain tools and libraries for the data science pipeline.
4. Clearly communicate results to stakeholders through verbal and written communication.
5. Share ideas with the wider team, learn from and contribute to the body of knowledge.

Key skills

1. Experience using Python and SQL.
2. Strong proficiency with PyData stack.
3. Understanding of web frameworks such as FastAPI.
4. Knowledge of machine learning techniques and their respective pros and cons.
5. Confident communicator and contributes effectively within a team environment.
6. Self-driven and willing to lead on projects / new initiatives.

Nice to haves

1. Strong SQL and interest in data engineering.
2. Deep learning model development experience.
3. GPU inference and deployment experience.

Interview

1. Initial call with TA.
2. Take home task.
3. Task debrief and case study interview.
4. 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!

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.

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