Address:

Hybrid Work Model

Researches, builds, and implements scalable artificial intelligence systems capable of learning and making predictions to business requirements. Enhances data pipelines and lakes to ensure data is clean, accurate, and optimized for machine learning models. Monitors, evaluates, and optimizes learning processes to continuously improve high-performance models. Works with other data and analytics professionals to optimize, refine, automate and scale analysis into repeatable analytics solutions and decision support tools.

• Designs and develops machine learning (ML) and deep learning systems.
• Runs machine learning tests and experiments. Trains and retrain systems to prevent drift and optimize results.
• Solves complex problems with multi-layered data sets, extends existing ML frameworks and optimizes existing machine learning libraries.
• Develops Machine Learning apps, implements algorithms, and builds tools to apply ML frameworks.
• Turns unstructured data into useful information by auto-tagging images and text-to-speech conversions.
• Develops ML algorithms to analyze huge volumes of historical data to make predictions.
• Runs tests, performs statistical analysis, and interprets test results.
• Focus is primarily on business/group within BMO; may have broader, enterprise-wide focus.
• Exercises judgment to identify, diagnose, and solve problems within given rules.
• Works independently on a range of complex tasks, which may include unique situations.
• Broader work or accountabilities may be assigned as needed.
• Take measured risks while protecting the bank by applying our Risk Management Framework in the execution of your role, in line with our Risk Culture and within our approved Risk Appetite, making sound and risk informed decisions that align to business strategy, protect assets, and adhere to applicable policy documents (Frameworks, Policies, Standards, Procedures and Supporting documents), laws and regulations.

Qualifications:

Foundational level of proficiency:

• Systems Thinking.
• Mathematics, Statistics & Operations Research.
• Critical thinking.
• Creative reasoning.

Intermediate level of proficiency:

• Computational Thinking and Programming.
• Deep Learning.
• Machine Learning.
• Scaling Models.
• Continuous Integration and Continuous Delivery/Deployment.
• ML algorithm.
• Verbal & written communication skills.
• Collaboration & team skills.
• Analytical and problem solving skills.
• Data driven decision making.
• Typically between 4 - 6 years of relevant experience and post-secondary degree in related field of study or an equivalent combination of education and experience.
• Technical proficiency gained through education and/or business experience.

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 structure. Salaries for part-time roles will be pro-rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group’s expected target for the first year in this position.

BMO Financial Group’s total compensation package will vary based on the pay type of the position and may include performance-based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. To view more details of our benefits, please visit: