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Summary: Machine learning and Artificial Intelligence (ML and AI) is a strategic initiative in risk management. The candidate will participate in new projects and be hands-on along side internal experts. The candidate will have a concrete and meaningful contribution to the final deliverables and is expect to show autonomy and ownership of their deliverables.
The candidate will be responsible for identifying AI and ML use cases in the Risk Capital and Model Development group and building these models in partnership with the existing team. This will cover multiple areas such as Anti-Money Laundering, Credit Risk, Economic Capital etc.
The candidate will also be working on identifying automation opportunities where existing processes can be enhanced and simplified using AI.
The role also has a technology component where the candidate will participate in the assessment of the current risk technology stack and test and validate assumptions on new tools.
Description: - Use case identification:
Expand on the currently identified use cases by partnering with the existing model development groups and the business partners and identify other types of models/applications where ML and AI can add value. This will cover areas such as credit risk, economic capital, AML and market risk.
Structure above identified use cases, define operating model, manage delivery and provide - Data collection and manipulation
Collect data for model development purposes by identifying new candidate variables and predictive variables.
Analyze and summarize data to identify outliers and assess data quality.
Perform data imputation using advanced statistical techniques (multiple imputation, machine learning estimations).
Transform and prepare the data for developing AI models. - Predictive models development
Fully understand the existing version of the models where applicable, their limitations and all the underlying assumptions made.
Use the prepared data to build machine learning predictive models using advanced regression techniques such as random forests, neural networks, boosted trees, deep learning, logistic regressions, NLP etc...
Rank and compare the candidate models to select the best fitting models.
Use statistical tests to ensure the stability and robustness of the candidate models.
Identify new opportunities to apply machine learning in the current model development process. - Reasonableness assessment
Ensure that the models developed make business and intuitive sense by participating in sessions with the model owners, lines of businesses and including their feedback in the model.
Design a process to explain the output of the models on a consistent basis to understand why the models are producing these decisions. - Documentation
Document the process along the way by capturing the details around all assumptions made.
Keep record and detailed notes about the thought process. The models developed will serve as a proof of concept for future ML & AI models in credit risk.
Qualifications Skills required:
Master or PhD candidate in Engineering, Statistics, Mathematics, Computer Science or related quantitative field.
5-10 years of experience in AML, risk management and machine learning and AI techniques
Familiarity with risk management processes and methodologies, ideally AML
Familiarity with Bank credit instruments and product structures
Familiarity with systems management and data architecture principles
Experience with statistical model development and data mining
Proven experience using SAS, R or Python to build machine learning models
Strong theoretical knowledge of machine learning techniques and advanced regression methods and data imputation techniques
Well-developed relationship management skills.
Excellent influencing and negotiation skills
Strong problem solving skills and capacity to turnaround analysis in short period of time
Comfortable in a challenging environment with tight timelines
Excellent written and verbal communication skills
Capacity to cope with a high degree of ambiguity and change
Ability to work both independently and as part of cross-functional teams
Prior industry experience or academic projects with machine learning and advanced programming is preferred.
High-level of competency with MS Office suite of tools
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At BMO we have a shared purpose; we put the customer at the centre of everything we do - helping people is in our DNA. For 200 years we have thought about the future-the future of our customers, our communities and our people. We help our customers and our communities by working together, innovating and pushing boundaries to bring them our very best every day. Together we're changing the way people think about a bank.
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://bmocareers.com .
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.