Data Engineer, Fraud Predictive Analytics
Role Overview
Applies advanced analytics and technologies to develop predictive models and intelligent automation for business decisions, customer experience, and productivity.
Develops Data and AI products for the Financial Crimes Unit, including fraud detection, investigation, and prevention tools and models.
Hybrid role.
Key Skills for This Role
Full Job Posting
Role overview
Applies advanced analytics and technologies to develop predictive models and intelligent automation for business decisions, customer experience, and productivity.
Develops Data and AI products for the Financial Crimes Unit, including fraud detection, investigation, and prevention tools and models.
Hybrid role.
Responsibilities
- Research and recommend practical solutions to financial crime and criminal-risk exposures, and understand related management processes and frameworks.
- Analyze complex business problems, formulate data-driven hypotheses, and implement solutions based on business implications.
- Support data collection, integration, and retention; discover useful internal and external data sources.
- Build data solutions and prepared datasets from structured, semi-structured, and unstructured data, including data at multiple levels of granularity.
- Apply statistical, machine-learning, deep-learning, and AI techniques to test hypotheses, discover patterns, and develop analytical solutions.
- Diagnose model-performance issues, monitor system performance, and implement efficiency improvements.
- Communicate findings and business conclusions through clear, actionable analysis and storytelling.
- Build stakeholder relationships and support development of analytics and AI tools and training.
Qualifications
- Typically 5–7 years of relevant Data Science and Analytics experience and a related post-secondary degree, or equivalent education and experience.
- Advanced degree in a quantitative discipline or equivalent experience is cited as a qualification.
- Experience with programming languages such as SQL, Python, or PySpark, and machine-learning or deep-learning packages such as XGBoost, H2O, or SparkML.
- Deep proficiency in statistical and quantitative analysis, forecasting and predictive analytics, multivariate testing, and optimization algorithms.
- Experience building fraud investigation, detection, or prevention models for a large financial institution.
Compensation
- Advertised salary range is $80,000–$175,000; pay type is salaried.
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