Senior Data Scientist

My client works in the Insurance / Risk Management space and is relatively well established, having served their clients over the last 12 years. The firm was a relatively late adopter of AI, mostly due to some of the red tape and regulations affiliated with their more traditional sector. However, with a new CEO onboard and a more pragmatic approach, the firm is keen to play catch-up and help revolutionise their industry as others are doing. To help accelerate this journey, they’ve invested heavily in the AI team and have now got some heavy-hitters in to lead on some cool, transformational projects. With a few MLEs already hired, they’re now looking for a senior MLOps individual to spearhead cloud deployment and management of some of the Key ML pipelines / infrastructure.

Day-to-Day Responsibilities:

• Design, implement, and maintain robust MLOps pipelines to ensure seamless deployment, monitoring, and scaling of machine learning models in production.
• Collaborate within the team to operationalise models, ensuring they are scalable, reliable, and efficient.
• Develop and maintain CI/CD pipelines for ML workflows, integrating automated testing, model validation, and version control.
• Monitor model performance in production, identifying and resolving issues such as data drift, model degradation, and latency bottlenecks.
• Optimise cloud infrastructure for machine learning workloads, ensuring cost-efficiency and scalability.
• Document processes, workflows, and best practices to ensure knowledge sharing and continuity within the team.

It goes without saying, but given the novelty of MLOps roles on the whole, the engineer should be keen on keeping up with best practices, attending workshops / events (on company time) and ensuring that they stay at the top of their game.

Technical Expertise:

• Strong experience with cloud platforms such as AWS or Azure, including services like SageMaker, MLflow / Kubeflow.
• Solid understanding of CI/CD tools (Jenkins, GitLab CI, GitHub Actions) and version control systems (aka Git).
• Experience with IAC - Terraform or CloudFormation.
• Nice to haves: Familiarity with data engineering tools / frameworks (Apache Spark / Airflow) for pre-processing and managing large datasets.
• Experience of working within the Insurance / Risk sector is really beneficial but not essential.

Good allowance for continued learning / development – bolstered by a £2,200 individual yearly learning fund. Flexible working to suit care / caregiving needs. Cycle to work schemes / season ticket initiatives. 27 days of annual leave rising to 30 after 3 years of service.