Machine Learning Engineer

BMO•Vancouver, BC

4d•Hybrid

About The Position

We are seeking a highly analytical and technically proficient ML/AI Engineer to join our ARC team. This role is ideal for someone with a strong foundation in mathematics, statistics, and programming, and a passion for applying AI to solve complex financial problems. You will work to develop AI/ML/DS features for enterprise-wide AI products, develop models, optimize strategies, and contribute to the evolution of our AI-powered financial systems.

Requirements

Master’s or Ph.D. in Mathematics, Statistics, Computer Science, Data Science, Physics, AI, Machine Learning or a related field.
Experience in model development (ML/ data science, AI/GenAI) within financial services or technology sectors.
Proficiency in Python and SQL, TensorFlow, PyTorch, XGBoost, scikit-learn
Strong grasp of artificial intelligence and machine learning frameworks and stacks.
Intellectual curiosity and adaptability to emerging AI and quant finance trends.
Strong communication skills to explain complex models to non-technical stakeholders.
Ability to work independently and collaboratively in a fast-paced, multidisciplinary environment.
Attention to detail and a rigorous approach to model validation and testing.

Nice To Haves

Familiarity with cloud platforms (AWS, Azure, GCP) and CI/CD pipelines is advantageous.

Responsibilities

Design and develop machine learning models (Supervised, Unsupervised, and Reinforcement Learning), AI (Generative models and agent orchestration) models, and deep learning models (e.g., Neural Networks and autoencoders).
Run machine learning tests and experiments.
Train and retrain systems to prevent drift and optimize results.
Solve complex problems with multi-layered data sets, extend existing ML frameworks (Scikit-Learn, XGBoost, Tensorflow) and AI frameworks (Keras, LangChain).
Leverage and develop advanced analytics models (network based, forecasting, rules-based), implement said algorithms, and build tools to apply them.
Turn structured, semi-structured and unstructured data into useful information
Develop ML/AI algorithms to analyze huge volumes of historical data to derive insights, make decisions, and form predictions.
Run tests, perform statistical analysis, and interpret test results.
Contribute to shaping the digital foundations: (Hypergraph) Scenario Engine and Network based Methods: graph-based modeling tool that maps relationships between entities and simulates cascading scenarios; Chatbots (i.e., Distribution); Semantic Engine: AI layer that enables meaning-based search as opposed to keyword search.
Conduct large-scale analysis of information to discover patterns and trends by combining different models and algorithms.
Get insights from data (descriptive, attribution).
Perform Topological Data Modeling, Causality, Variable Importance Analysis, Attribution modeling, Regression.