Accenture13 followers232 jobs

Data Science Manager – Fraud Risk Engine

Mid levelSeniorFull-timeHybrid5/161 Collins St, Melbourne VIC 3004, AustraliaSydney NSW, AustraliaCanberra ACT, AustraliaProfessional servicesDataInformation technologyPosted 1 month ago
Pending fitX of Y criteria met

About the job

The Fraud Risk Engine Decision Scientist job is all about leading the design and implementation of real-time fraud detection strategies using a fraud risk engine. This position plays a crucial role in bridging business needs with technical expertise, fostering proactive fraud prevention through data-driven decision-making. Collaboration is key, as you will work closely with various stakeholders to ensure timely and accurate fraud detection.

Meet the team

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You'll be responsible for

🤝

Partnering with business teams

Partner with business teams to define and refine fraud detection use cases based on emerging threats, customer behaviour, and regulatory needs.
🔍

Data sourcing and cleaning

Identify, source, clean, and organise relevant data to support fraud detection. Perform exploratory data analysis to uncover patterns and insights.
⚙️

Configuring fraud risk engine

Configure and tune rules, thresholds, and predictive models within the fraud risk engine to enable real-time decisioning.

Key criteria

🔍

5+ years in fraud and financial crime analytics

Extensive experience in data science within fraud detection environments.

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💻

Proficiency in programming languages

Strong skills in Python, R, Scala, or Java for data analysis.

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📊

Knowledge of fraud detection technologies

Experience with rules-based, machine learning, and AI-driven solutions.

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5 criteria for this job
5+ years in fraud and financial crime analytics
Extensive experience in data science within fraud detection environments.

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Proficiency in programming languages
Strong skills in Python, R, Scala, or Java for data analysis.

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Knowledge of fraud detection technologies
Experience with rules-based, machine learning, and AI-driven solutions.

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Hands-on experience with AWS
Familiarity with AWS data services for fraud detection pipelines.

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Understanding of machine learning techniques
Solid grasp of techniques like random forests and ensemble methods.

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