Permanent Data Science And Analytics Jobs in New South Wales
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Fraud Data Scientist
Title: Fraud Data Scientist Compensation: $140k - $165k base + bonus Company Summary: A leading consulting firm is seeking a Fraud Analytics Senior Associate/Data Scientist to help clients across financial services, fintech, and payments strengthen fraud detection capabilities. The role combines fraud strategy, machine learning, and advanced analyt...
AI Engineer
Applied AI EngineerNYC, hybrid We're partnered with a leading quantitative investment and technology firm that is building out a new generation of AI-powered tooling and looking to hire an AI Engineer to help drive the initiative.This is a highly hands-on role focused on identifying real business problems and building AI-driven solutions that impro...
Mortgage Analytics Developer
Mortgage Analytics Developer - Fixed Income and MortgagesOur client is a highly respected New York-based alternative asset manager focused on structured credit, mortgages, and asset-backed investments. The team combines deep fundamental credit expertise with sophisticated quantitative analytics and technology, providing direct exposure to portfoli...
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Data Analyst
Data Analyst | Quantitative Trading FirmOverviewJoin a leading quantitative trading firm where data, technology, and research drive business performance.Work with large-scale financial, market, vendor, and alternative datasets that support critical business and research functions.Partner with engineers, data professionals, and business stakeholders...
Senior Data Engineer
ResponsibilitiesBuild and optimize scalable data pipelines and data products.Develop reliable datasets for analytics, reporting, and AI applications.Establish data engineering standards, frameworks, and best practices.Improve data quality, monitoring, testing, and platform observability.Integrate and reconcile complex datasets from multiple sources...
Senior Data Engineer
ResponsibilitiesBuild and optimize scalable data pipelines and data products.Develop reliable datasets for analytics, reporting, and AI applications.Establish data engineering standards, frameworks, and best practices.Improve data quality, monitoring, testing, and platform observability.Integrate and reconcile complex datasets from multiple sources...