MLOps & Engineering: Collaborate with Data Engineers to productionalize models. You will help maintain the ML lifecycle, including versioning, monitoring model drift, and automating retraining loops.
Partner with business product owners, technology teams, and senior stakeholders to provide trusted technical advice, influence strategic decisions, and foster adoption of data science solutions across the organisation.
Contribute to data science and AI initiatives, including Mosaic, Audience Taxonomy, Spend Analytics, embeddings, retrieval-augmented generation (RAG) and prompt engineering, where relevant.
Contribute to data science and AI initiatives, including Mosaic, Audience Taxonomy, Spend Analytics, embeddings, retrieval-augmented generation (RAG) and prompt engineering, where relevant.
Contribute to data science and AI initiatives, including Mosaic, Audience Taxonomy, Spend Analytics, embeddings, retrieval-augmented generation (RAG) and prompt engineering, where relevant.
Contribute to data science and AI initiatives, including Mosaic, Audience Taxonomy, Spend Analytics, embeddings, retrieval-augmented generation (RAG) and prompt engineering, where relevant.
Contribute to data science and AI initiatives, including Mosaic, Audience Taxonomy, Spend Analytics, embeddings, retrieval-augmented generation (RAG) and prompt engineering, where relevant.
Contribute to data science and AI initiatives, including Mosaic, Audience Taxonomy, Spend Analytics, embeddings, retrieval-augmented generation (RAG) and prompt engineering, where relevant.
You will size opportunities and run proactive deep dives into consumer, merchant and driver-partner behaviour that shape the roadmap rather than report on it.
You will size opportunities and run proactive deep dives into consumer, merchant and driver-partner behaviour that shape the roadmap rather than report on it.
Design, measure, and recommend A/B tests and multivariate experiments, including layout optimisation, UI/UX efficacy, algorithm effectiveness, and API performance.
Root-Cause Analysis & Problem Solving: Interact directly with stakeholders to resolve data quality issues using techniques like root-cause analysis, continuously improving trust and confidence in our enterprise data products
You will design, measure, and recommend A/B tests and multivariate experiments, including layout optimisation, UI/UX efficacy, algorithm effectiveness, and API performance.