Data Engineer, Fraud Predictive Analytics

Final date to receive applications:11/29/2026

Address:100 King Street West Job Family Group:

Data Analytics & Reporting Hybrid role Applies knowledge of advanced analytic algorithms and technologies (e.g. machine learning, deep learning, artificial intelligence) to deliver better prediction models and/or intelligent automation that enables smarter business decisions, improved customer experience, and drives productivity. Applies strong communication and story-telling skills to summarize statistical/algorithmic findings, draw business conclusions, and present actionable insight in a way that resonates with business/groups.

Drives innovation through the development of Data & AI products that can be leveraged across Financial Crimes Unit to build advanced fraud detection, fraud investigation, fraud prevention tools/models. Conducts research to identify, assess and recommend viable, practical solutions to specific financial crimes /criminal risk exposures

Develops and maintains an understanding of the financial crime management processes, frameworks and techniques.

Recommends and implements solutions based on analysis of issues and implications for the business.

Understands and analyzes complex business problem, then formulates data-driven hypotheses to drive business value.

Builds effective relationships with internal/external stakeholders and ensures alignment.

Supports data collection, integration, and retention requirements for data.

Defines innovative data solutions to loosely defined business problems by leveraging pattern detection over potentially large datasets.

Diagnoses and resolves predictive / analytical model performance issues while monitoring system performance and implementation of efficiency improvements.

Develops analytical solutions and makes recommendations based on an understanding of the business strategy and stakeholder needs.

Works with various data owners to discover and select available data from internal sources and external vendors (e.g. financial crime data) to fulfill analytical needs.

Applies scripting / programming skills to assemble various types of source data (unstructured, semi-structured, and structured) into well-prepared datasets with multiple levels of granularities (e.g., demographics, customers, products, transactions).Develops agreed analytical solution by applying suitable statistical & machine learning techniques (e.g., A/B testing, prototype solutions, mathematical models, algorithms, machine learning, deep learning, artificial intelligence) to test, verify, refine hypotheses.

Uses the appropriate algorithms to discover patterns.

Supports development of tools and delivers training for data analytics and AI.Broader work or accountabilities may be assigned as needed.

Qualifications:

Typically between 5 - 7 years of relevant experience in Data Science & Analytics and post-secondary degree in related field of study or an equivalent combination of education and experience.

Advanced degree in Computer Science, Mathematics, Physics, Engineering, Statistics, or other quantitative disciplines and/or equivalent experience

Experience with programming languages (e.g. SQL, Python, PySpark) and machine learning /deep learning algorithms/packages (e.g. XGBoost, H2O, SparkML).Deep proficiency in statistical analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and optimization algorithms.

Experience building fraud models (investigation, detection, prevention) for a large Financial Institution Salary :$80,000.00 - $

Pay Type:

Salaried The above represents BMO Financial Group’s pay range and type.

Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission…