Lead/Senior Machine Learning Engineer
- £110,000-£120,000
- Bonus up to 10%
- Shares so as they continue to grow you benefit to
- Hybrid working - one day a week London (with door always open policy)
Are you a innovative, decisive Machine Learning Engineer looking for your next challenge?
This is your chance to join a marquee name within the fin-tech space looking to add their first Machine Learning Engineer to the business, this will require you to be a key individual contributor with the ability to make decisions yourself.
Within the role you will drive innovation by optimising and automating Pricing processes to enable faster, more accurate decision-making. Your work will focus on developing and maintaining tooling and frameworks that enhance the efficiency of our predictive models, reducing deployment times, increasing scalability, and improving model performance through regular updates and monitoring.
You will work closely with the Data Scientists, Actuaries, and Product team to deliver scalable, production-grade ML systems.
This is a super exciting time to join the business who after a number of years of great success have hit profitability and now want to grow through strategic hiring.
Key Responsibilities
- Build model lifecycle tooling (deployment, monitoring and alerting) for our predictive models (for example claims cost, conversion, retention, market models)
- Maintain and improve the development environment (Kubeflow) used by our Data Scientists and Actuaries
- Develop and maintain pricing analytics tools that enable faster impact assessments, reducing manual work
- Collaborate with the technical pricing, street pricing and product teams to gather requirements and feedback on tooling and to build impactful technology
- Communicate complex concepts to technical and non-technical stakeholders through clear storytelling
Required Skills
- Education: Bachelor's or Master's degree in Statistics, Data Science, Computer Science or related field
- Experience: Proven experience in ML model lifecycle management
● Core Competencies:
- Model lifecycle: You've got hands-on experience with managing the ML model lifecycle, including both online and batch processes
- Statistical Methodology: You have worked with GLMs and other machine learning algorithms and have in-depth knowledge of how they work
- Python: You have built and deployed production-grade Python applications and you are familiar with data science libraries such as pandas and scikit-learn
- Tooling & Environment: ○ DevOps: You have experience working with DevOps tooling, such as gitops, Kubernetes, CI/CD tools (we use buildkite) and Docker
- Cloud: You have worked with cloud-based environments before (we use AWS)
- SQL: You have a good grasp of SQL, particularly with cloud data warehouses like Snowflake
- Version control: You are proficient with git
Soft Skills:
- You are an excellent communicator, with an ability to translate non-technical requirements into clear, actionable pieces of work
- You have proven your project management skills, with the ability to manage multiple priorities
Interested in finding out more? Click apply to be considered for shortlisting.
Burns Sheehan Ltd will consider applications based only on skills and ability and will not discriminate on any grounds.
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