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Data Obsessed Software Engineer

London · Full time

Quin is a 12 strong, female-founded, digital health startup building products for chronic health conditions. Our first app, which helps people with Type 1 Diabetes make decisions about their self-care, was launched in the UK last year. We’re now looking for a multi-talented, data obsessed software engineer to build the data pipelines and infrastructure that mean we can create more personalised experiences for our users.

The Quin approach uses science, engineering, and design to build products that empower people to make better decisions and provide personalised support. This new role will allow you to balance your data and engineering skills. You’re a generalist – part data engineer, part data scientist with a focus on personalisation and advanced analytics, and part software engineer. You bring solid data and machine learning engineering skills and you are looking for a deeper, more complex problem with enormous social impact on which to work. 


You will get to:

  • Embed in our cross-functional product team (product managers, designers, ML engineer, software engineers) to put the data deployment infrastructure in place for us to build new data products, then help us build them.
  • Help us define the personalisation strategy for Quin. This is a new part of our work so you’ll be happiest picking a path out of undefined and messy ideas and creating a route forward without all of the data. You love relying on logic, collaboration and the scientific method, rather than your previous experience.
  • Be involved in the end-to-end product development life cycle – from ideas through discovery, to launch of products and features – but nothing gets you more excited than delivering something our users love.
  • Set things up and make informed decisions, often with incomplete information. We need someone to choose the appropriate sized solution for the immediate problems rather than designing a perfect architecture with all the latest enterprise-level tools.
  • Join a glorious team of big brained, big hearted people who love board games and cake, who are currently remote but looking to move to a hybrid office/home working pattern once it’s safe.


You’ve done or are interested in the following:

  • Python, SQL and related tools, libraries and frameworks like Numpy, Pandas, Scikit-learn, Flask and SQLAlchemy. You like writing well tested, readable and performant code, capable of processing large volumes of data.
  • Evaluating and using off-the-shelf ML platforms and data products such as Databricks/Spark, AWS Sagemaker and Kafka.
  • Designing infrastructure around data platforms, preferably with serverless deployment configuration.
  • Experience and understanding of the analytical and machine learning techniques used to drive in-app personalisation.
  • Practical knowledge of algorithms and techniques both supervised (ML) and unsupervised (classification, clustering, recommender system, different regression techniques.)
  • Core ML skills; you’re familiar with tuning models, you know how to debug and test a machine learning model, how to spot and solve overfitting and bias problems, how to scale features and improve data to make your algorithms work better.


This is the role where all your experience and interests could get used: your work building data pipelines, your expertise in data science and machine learning, your agile software engineering skills, your interest in psychology and how the human body works and your ability to see the wood for the trees and prototype solutions to undefined, hard problems. We love meeting the whole human, so we’re not just interested in your engineering skills!

Quin challenges conventional thinking and the received wisdom of the medical and pharmaceutical worlds. We work with a massively diverse set of user needs, turning human experience into science. We believe the more inclusive we are, the better our product will be. To do that, we’ve created an environment where everyone can bring their whole selves to work, and we welcome applications from all.


If you would like to find out more, please email [email protected]