26 to 29 of 29 Data Cleansing Jobs in Slough

to identify improvement opportunities and roll out new working practices 🔸 Manage external vendors and partners to ensure timely and high-quality delivery 🔸 Support data reconciliation and cleanse activities to ensure compliance and integrity Key Skills Required ✅ Experience in at least 3 Oracle Cloud ERP implementations , including one … process re-engineering , with a focus on aligning with Oracle Cloud’s standard processes ✅ Proven track record with data migration, reconciliation, cleansing, and purging ✅ Ability to identify and manage integrations with third-party applications Join a strategic Oracle Cloud programme and lead critical enhancements that will More ❯

workflows and integrations with existing systems/databases Tailor the platform to reflect local authority-specific processes Conduct data audits and cleansing to ensure accuracy Migrate from any legacy systems with zero data loss Validate and test all transferred data More ❯

We trade across a range of frequencies. You will be involved with the entire scope of the signal generation process including: Idea generation Data sourcing, cleaning, and setup Cutting-edge data modelling and statistical analysis Real-world implementation Prior experience in all of these … evaluation process. A live trading track record is not required, but you should be ready to discuss your ideas. Experience working with alternative data or large datasets is a plus. Do not submit anything that is confidential or proprietary. The best candidate will have good software skills More ❯

large-scale ML models. Build and maintain experiment tracking, monitoring, and observability systems. Collaborate with SWE and infra colleagues to build tooling for data access, cleaning, and delivery. Contribute to the internal “toolbox” enabling repeatable, scalable ML deployment across client teams. Work closely with researchers and strategy … PyTorch (or similar) environments, with experience in multi-node training and scale-up workflows. Deep understanding of ML Ops best practices : experiment tracking, data/version control, reproducibility. Ability to diagnose and tune model performance (both training and inference). Comfort navigating hybrid infrastructure : some workloads will More ❯