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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and evolving AI benchmarking platforms, with strong proficiency in Python and experience deploying machine learning workloads to cloud platforms. Capable of translating complex data insights into actionable recommendations while managing fast-paced workstreams autonomously.
Highest-signal resume keywords
Python ProgrammingData Science ConceptsML Workload DeploymentCI/CD and ContainerisationExperimental Design
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 ScienceLLM ExperimentationAsynchronous ProgrammingMultithreadingMaintainable CodeStatisticsEvaluation FrameworksBenchmarking StrategiesCloud PlatformsRobust Engineering
Soft Skills
Clear CommunicationInitiativeInfluencing Technical DirectionSupporting Development of OthersAutonomous Work Management
Tools & Technologies
AzureAWSGCPDockerPodman
Industry Keywords
AI BenchmarkingExperimentation InfrastructureClient EngagementsTechnical DemosBusiness-Ready Insights
Tech Stack
Tools & technologiesAWSAzureCloudDockerGoogle Cloud PlatformPython
About the role
Key responsibilities & impact- Build and evolve AI benchmarking and experimentation platforms
- Design and run end-to-end benchmarking workflows based on client use cases
- Design and run benchmarking strategies and generate business-ready insights
- Build scalable evaluation frameworks, metrics and pipelines
- Review academic literature to ensure evaluation strategies reflect leading research and best practice
- Develop, maintain and improve experimentation infrastructure
- Ensure robustness and production-grade engineering
- Produce clear, client-ready insights
- Support technical demos and deep-dive sessions
- Influence AI model selection and technical strategy across PwC projects and client engagements
Requirements
What you’ll need- Strong hands-on experience in Data Science concepts or LLM experimentation using structured evaluation frameworks
- Highly proficient in Python, including asynchronous programming, multithreading and writing maintainable code
- Experience deploying ML workloads to cloud platforms (Azure, AWS or GCP)
- Familiarity with CI/CD and containerisation (Docker/Podman)
- Applied knowledge of statistics and experimental design
- Ability to translate findings into actionable recommendations
- Comfortable managing fastmoving workstreams and operating autonomously
- Emerging leadership behaviours, including taking initiative, influencing technical direction, communicating clearly and supporting the development of others
- Available for work visa sponsorship
- Willingness to travel up to 20%
Benefits
Comp & perks- Empowered flexibility and a working week split between office, home and client site
- Private medical cover
- 24/7 access to a qualified virtual GP
- Six volunteering days a year
- Fair recognition and rewards for contributions