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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in Python programming and the deployment of machine learning models in production environments, with a strong focus on CI/CD practices and collaboration across teams. Proficient in leveraging cloud-native solutions and containerization technologies to automate the data science lifecycle.
Highest-signal resume keywords
Python ProgrammingMachine Learning EngineeringCI/CD AutomationCloud-Native DeploymentContainerization and Kubernetes
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data Science PrinciplesModel DevelopmentProduction-Level DeploymentGenerative AILLMsAzureDatabricksGitAzure DevOpsJIRA
Soft Skills
Strong Communication Skills
Tools & Technologies
CI/CD ToolsKubernetesAzureDatabricksGitAzure DevOpsJIRA
Industry Keywords
Regulated IndustryFinanceInsurance
Tech Stack
Tools & technologiesAzureCloudKubernetesPython
About the role
Key responsibilities & impact- Contribute to the design and evolution of the Data Science platform
- Help define best practices, tooling, and the ML Engineering function
- Automate the end-to-end data science lifecycle using CI/CD and infrastructure as code
- Collaborate across the data science and deployment lifecycle for traditional machine learning and generative solutions
- Work with data engineers, software engineers, and business stakeholders
- Write high-quality Python code for model development, deployment, and maintainability
- Contribute to modelling and project workflows
- Help select modelling approaches and participate in architecture discussions and deployment strategies
- Transition research models into scalable, production-ready solutions
Requirements
What you’ll need- Proven track record in data science or ML engineering roles within a business setting
- Strong Python programming skills and wider software engineering best practice
- Strong communication skills, including translation of technical concepts for non-technical stakeholders
- Good understanding of core data science principles
- Experience with production-level cloud-native deployment of machine learning services
- Experience using containerisation and Kubernetes or equivalent
- Experience with Azure and Databricks particularly beneficial
- Experience with Git, CI/CD, Azure DevOps desirable, and JIRA
- Experience deploying data science models to solve real-world business problems in production
- Experience ideally within a regulated industry such as finance or insurance
- Experience utilising LLMs, generative AI, or agentic AI in a commercial setting beneficial
Benefits
Comp & perks- Competitive salary
- Retirement plans
- Healthcare coverage
- Flexible working options
- Professional development support
- Financial support for professional qualifications
- World-class technical training
- Courses focused on personal growth, career progression, and leadership skills
- Hybrid working model
- Independence and flexibility