Responsibilities
- Build and support ML lifecycle tooling for model deployment, monitoring, and alerting
- Maintain and improve the Kubeflow environment for Data Scientists and Actuaries
- Create pricing analytics tools to accelerate impact analysis and reduce manual work
- Collaborate with pricing and product teams to deliver high-impact tooling
- Communicate complex concepts clearly to technical and non-technical audiences
Basic qualifications
- Bachelor’s or Master’s degree in Statistics, Data Science, Computer Science, or a related field
- Strong experience managing the full ML model lifecycle (batch and online)
- Solid understanding of statistical methods, including GLMs and modern ML techniques
- Proven ability to build and deploy production-quality Python applications (pandas, scikit-learn)
- Experience with DevOps and ML tooling, including Kubernetes, Docker, CI/CD, and git-based workflows
- Familiarity with cloud platforms (AWS) and cloud data warehouses (Snowflake/SQL)
Benefits
Salary: to be discussed, depending on experience
Length: 6 months, with the view to extend
Tags & focus areas
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