Who are we?
Look at the latest headlines and you will see something Ki insures. Think space shuttles, world tours, wind farms, and even footballers’ legs.
Ki’s mission is simple. Digitally disrupt and revolutionise a 335-year-old market. Working with Google and UCL, Ki has created a platform that uses algorithms, machine learning and large language models to give insurance brokers quotes in seconds, rather than days.
Ki is proudly the biggest global algorithmic insurance carrier. It is the fastest growing syndicate in the Lloyd's of London market, and the first ever to make $100m in profit in 3 years.
Ki’s teams have varied backgrounds and work together in an agile, cross-functional way to build the very best experience for its customers. Ki has big ambitions but needs more excellent minds to challenge the status-quo and help it reach new horizons.
Where you come in?
Working within the Algorithmic Underwriting team, the Data Scientist focusses on the build, deployment, and safe operation of Ki's suite of risk modelling and risk selection algorithms. They collaborate with product, engineering, actuarial, and underwriting teams to develop models, experiment with new datasets and features, and integrate these into underwriting and portfolio management capabilities.
What you will be doing: ️
- Work with other data scientists, actuaries, engineers, and commercial teams to deliver and maintain production-grade machine learning models at scale.
- Carry out regular monitoring, assessments, and optimisation of live data, models, and the underwriting algorithm to identify opportunities for improvement.
- Explore new ideas and emerging statistical, agentic and LLM, and machine learning approaches and technologies to understand how they can be embedded into the Ki algorithm or wider business processes.
- Drive improvements in the way we operate as a digital underwriting capability
- Manage and/or support the development of junior members of the team
Requirements
- Hands-on Data Science experience (or a related role) within financial, fintech, or otherwise financial risk and predictive modelling context.
- Comfortable working in Python for production environments, experience with common software development and machine learning frameworks (e.g. GitHub, Copilot, Claude, CI/CD, scikit-learn, TensorFlow, LangChain).
- Experience taking data science models from research and development into production
- A strong understanding of Machine Learning approaches and algorithms and how they are monitored and maintained.
- Experience working within cloud environments would be beneficial (GCP a plus)
- Experience working within a regulated industry, ideally working in the London and Lloyd’s insurance markets.
- Bachelor’s Degree or higher in a STEM field. PhD in a STEM field is a plus.
What to expect during the recruitment process:
- Initial recruiter screening call
- Interview with hiring manager
- Technical Interview (this may vary depending on the role)
- Values Interview