Data Scientist Researcher in London

Neko is redefining what prevention means, from treating illness when it arrives, to sustaining health before it's ever at risk. Our mission: make data-driven, preventative care accessible to more people, before symptoms appear. In a single, non-invasive visit under an hour, proprietary technology and direct clinical care combine to deliver personalised, actionable insights. It's a team that thinks in 10x, not 10%. Every role here plays a part in building a world where prevention is the norm, and where your work genuinely helps people live longer, healthier lives.

About the Role

A Data Scientist at Neko Health develops and implements data solutions that drive insights from sensor and healthcare data, enabling clinical decision support, and supporting innovations. This role partners with clinical researchers, engineers, and other teams to enhance products through data-driven insights.

Responsibilities

• Data Modeling and Analysis: Develop algorithms, statistical models and machine learning models to address targeted challenges using sensor and healthcare data. Develop performance evaluation and implement performance monitoring of data products to ensure that the intended value is delivered.
• Data Management: Prepare, clean, and integrate datasets from various sources, ensuring data quality and accuracy. Collaborate with Data Engineers to build and maintain scalable data pipelines for efficient analysis.
• Support for Clinical Studies: Collaborate with clinical teams to define targeted clinical hypotheses, study variables and collection methods for clinical studies. Perform exploratory analyses and provide data insights to refine study designs.
• Research and Innovation: Stay up-to-date on data science advances to continuously improve model accuracy, efficiency, and clinical relevance. Develop proofs-of-concept of new medical devices and methods. Support patent development.
• Maintenance, scaling, and common tooling: Improve robustness of products, streamlining of processes and increasing efficiency of data science development by applying good software engineering practices.

Requirements:

• MSc or PhD degree in a quantitative field for example Computer Science, Machine Learning or Physics.
• Expertise within data science, algorithm development, and statistics.
• Strong analytical abilities, collaborative mindset, and solid software engineering skills.
• A strong proficiency in designing and implementing machine learning algorithms.

About the Engineering Team

Distributed and Remote First. We are nearly 100 full time engineers at the company, working from Berlin, Chamonix, Hamburg, Lisbon, Marseille, Vilnius, and Stockholm, spanning diverse disciplines such as Hardware Engineering, Firmware Development, Electrical Design, Algorithm Development, Machine Learning Development, Optronics Research, Frontend Development and more. We don't expect people to join us with a specific tech knowledge, but we do expect you to work with our tools. We use a mix of React, Typescript, C++, and Python. Our APIs are written in C# with ASP.NET Core, uses Azure Cosmos DB, and Azure Active Directory for authentication.

Our headquarters and our hardware development team are in Stockholm, Sweden. We are a Remote First company; however, it is of course much easier to work remotely as a software engineer than a hardware or firmware engineer (since they require access to hardware or devices occasionally). Software engineers based in Stockholm work maybe one day a week or one day every two weeks from the office. We meet a couple of times per year to get to know each other and have fun.

Organization and Way of Working

The engineering team is divided into smaller cross functional project teams that each focus on a specific goal or target, where some groups are long-lived, and some are short-lived, depending on how big the goal or deliverable is. We strive to create groups which are cross-functional and able to complete their goals without dependence on other teams, even though this is of course not always possible. Groups track goals on a yearly and quarterly basis with goal follow-up across the entire engineering organization on a bi-weekly basis. Most groups do internal planning on a bi-weekly basis, but in the end it's up to the group to decide how they want to work. We have, however, mandated that all groups must present their progress or failures or hacks at our bi-weekly engineering demo, a fun meeting/presentation where we talk about everything from short-circuiting power-modules, how hard it is to calibrate cameras or align polygons in space, to neat new command line tools for operations, a new auth mechanism in the backend, a cool new way to visualize health data or a new feature which helps our doctors be more productive.

We have a flexible workplace that focuses on work/life balance, and we strongly believe in our mission but do not think that achieving it requires sacrificing everything else.