Machine Learning Engineer

BMO•Toronto, ON

4d•CA$82,800 - CA$154,800

About The Position

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. Focus is primarily on business/group within BMO; may have broader, enterprise-wide focus. Provides specialized consulting, analytical and technical support. Exercises judgment to identify, diagnose, and solve problems within given rules. Works independently and regularly handles non-routine 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.

Requirements

Foundational level of proficiency: Systems Thinking.
Intermediate level of proficiency: Mathematics, Statistics & Operations Research.
Intermediate level of proficiency: Critical thinking.
Intermediate level of proficiency: Creative reasoning.
Intermediate level of proficiency: Verbal & written communication skills.
Intermediate level of proficiency: Collaboration & team skills.
Intermediate level of proficiency: Analytical and problem solving skills.
Intermediate level of proficiency: Data driven decision making.
Advanced level of proficiency: Computational Thinking and Programming.
Advanced level of proficiency: Deep Learning.
Advanced level of proficiency: Machine Learning.
Advanced level of proficiency: Scaling Models.
Advanced level of proficiency: Continuous Integration and Continuous Delivery/Deployment.
Advanced level of proficiency: ML algorithm.
Typically between 5 - 7 years of relevant experience and post-secondary degree in related field of study or an equivalent combination of education and experience.
Deep knowledge and technical proficiency gained through extensive education and business experience.

Responsibilities

Designs and develops machine learning (ML) and deep learning systems.
Runs machine learning tests and experiments.
Trains and retrains 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.