100 King Street West
Job Family Group:
Audit, Risk & Compliance
Technology Risk Analyst is adept at building large data sets which mined and analyzed to identify risk, opportunity for optimization and improvement for product, service, process and risk controls pertaining to technology, resiliency, fraud, cyber and physical risk. The analyst should have strong experience using a variety of data mining/data analysis techniques, using a variety of data tools, using/creating algorithms and creating/running simulations. The analyst must have a proven ability to drive business results with their data-based insights. They must be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for building solutions to capture large data sets, which can be structured to provide analytical insights on technology, resiliency, fraud, cyber and physical risk.Responsibilities
Work with stakeholders in technology, resiliency, fraud, cyber and physical risk to identify opportunities for leveraging data.
Build data sets so that they can be mined and analyzed to identify risk, opportunity for optimization and improvement.
Assess the effectiveness and accuracy of new data sources and data gathering techniques.
Develop custom data models and algorithms to apply to data sets.
Position data sets to move from reactive to predictive base analysis.
Develop testing framework, includes quality and integrity criteria.
Coordinate with key areas that support technology, resiliency, fraud, cyber and physical risk to implement data sets, analytics and visualization tools.
Develop processes and tools to monitor and analyze performance and data accuracy.
Strong problem solving skills with an emphasis on technology and technology resiliency, infrastructure, network (includes architecture), application, API and cloud development.
Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
Experience working with and creating data architectures.
Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
Excellent written and verbal communication skills for coordinating across teams.
A drive to learn and master new technologies and techniques.
Broader work or accountabilities may be assigned as needed.
We're here to help
Typically 7+ years of relevant experience and post-secondary degree in related field of study or an equivalent combination of education and experience.
5-7 years of experience working with data sets and building statistical models, , Mathematics, Computer Science or another quantitative field, and is familiar with the following software/tools:
Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.
Experience using web services: Redshift, S3, Spark, DigitalOcean, etc.
Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
Experience analyzing data from 3rd party providers: Google Analytics, AWS / Azure, MS Teams, etc.
Experience with distributed data/computing tools: Hadoop, Hive, Spark, MySQL, etc.
Experience visualizing/presenting data for stakeholders using: Power BI, SageMaker, Periscope, Business Objects, D3, ggplot, etc.
In-depth / expert knowledge operational risk management practices.
In-depth / expert knowledge of the designated business / product portfolio.
In-depth / expert knowledge of regulatory requirements.
In-depth / expert knowledge of quantitative techniques and economic capital methodologies.
In-depth / expert knowledge & experience with risk policy frameworks; quality control / testing frameworks.
Seasoned professional with a combination of education, experience and industry knowledge.
Verbal & written communication skills - In-depth / Expert.
Analytical and problem solving skills - In-depth / Expert.
Influence skills - In-depth / Expert.
Collaboration & team skills; with a focus on cross-group collaboration - In-depth / Expert.
Able to manage ambiguity.
Data driven decision making - In-depth / Expert.
At BMO we are driven by a shared Purpose: Boldly Grow the Good in business and life. It calls on us to create lasting, positive change for our customers, our communities and our people. By working together, innovating and pushing boundaries, we transform lives and businesses, and power economic growth around the world.
As a member of the BMO team you are valued, respected and heard, and you have more ways to grow and make an impact. We strive to help you make an impact from day one - for yourself and our customers. We'll support you with the tools and resources you need to reach new milestones, as you help our customers reach theirs. From in-depth training and coaching, to manager support and network-building opportunities, we'll help you gain valuable experience, and broaden your skillset.
To find out more visit us at https://jobs.bmo.com/ca/en .
BMO is committed to an inclusive, equitable and accessible workplace. By learning from each other's differences, we gain strength through our people and our perspectives. Accommodations are available on request for candidates taking part in all aspects of the selection process. To request accommodation, please contact your recruiter.
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