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Risk Business Analyst

Delivery Hero — Germany · Posted ~2 hours ago

Junior Visa History ✓

Skills

Fraud Prevention Risk Modeling SQL Python Machine Learning A/B Testing Data Visualization (Looker, BigQuery) Stakeholder Collaboration Process Optimization Looker BigQuery

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Summary ✨ AI‑Generated

Join a leading on‑demand delivery company to fight fraud and protect revenue. You will analyze transaction data with SQL and Python, build machine‑learning‑driven risk rules, run A/B tests to validate controls, and partner with product and data science teams to continuously improve fraud prevention measures.

Highlights

Work on impactful fraud prevention projects using SQL, Python, and machine learning, design and test risk controls, collaborate with product and data science teams, and contribute to measurable profitability improvements in a fast‑growing delivery platform.

Description

Job DescriptionJob Ref ID: JR0084509 Be a part of a team where you will: Identify opportunities to reduce fraud losses on different streams and improve overall profitability by leveraging advanced risk rules and Machine Learning. Implement end-to-end fraud prevention strategies—design controls, execute rollouts, measure impact, and iterate for continuous improvement. Support the evaluation of fraud prevention controls by assisting in the setup, monitoring, and performance analysis of A/B tests. Collaborate with stakeholders like Product, Engineering, Data Science to propose innovative and preventive fraud controls. Perform risk analysis using SQL, Python and analytics platforms (e.g. Looker, BigQuery) to identify emerging fraud patterns and monitor key metrics. Contribute to machine learning workflows by partnering with Data Science: define relevant features, validate model outputs, and flag performance drift. Maintain and optimize a centralized escalation process for suspected fraud, ensuring timely reviews, feedback loops, and alignment with local teams to adapt risk controls regionally. Develop and maintain executive dashboards in Looker to track fraud OKRs, financial exposure, and compliance metrics. Drive clarity and resolve ambiguity on complex fraud topics, using data-driven approaches, simplifying concepts for different audiences (local ops, senior leadership, or external partners).