Senior Manager, Machine Learning

BMO•Toronto, ON

1d•CA$103,200 - CA$192,000•Hybrid

About The Position

This is a hybrid role in Toronto. The Senior Manager, Machine Learning leverages advanced analytical algorithms and technologies (e.g. machine learning, deep learning, artificial intelligence) to mine and analyze large sets of structured and unstructured data to obtain insights. Designs and constructs new processes for modeling data. Develops predictive models and leverages big data technology to design solutions that deliver smarter business decisions, improve customer experience, and drive productivity. Collaborates with other data and analytics professionals and teams to optimize, refine and scale machine learning solutions. This role involves end-to-end project management, continuous interaction with key stakeholders (strategy, implementation, business and model validation partners). Responsibilities include data mining or extracting usable data from multiple data sources, processing, cleaning, and validating the integrity of data to be used for analysis, and analyzing large amounts of information to find patterns and solutions. The role also involves developing prediction systems through the usage of machine learning tools and advanced statistics to select features, create and optimize classifiers or regression outcomes. The Senior Manager will present results in a clear and concise manner for non-technical stakeholders and provide comprehensive model documentation. They will also provide analysis and consultancy on model performance and recommend model usage, apply expertise and think creatively to address unique or ambiguous situations, and find solutions to complex and non-routine problems. The role is responsible for resolving issues raised by independent validation, Internal Audit and ongoing model monitoring. The Senior Manager is accountable for existing models in production and responsible for documentation related to existing models (annual assessment and re-validation, model updates).

Requirements

A post secondary degree in a quantitative field (i.e. mathematics, statistics, computer science, economics, engineering, physics, business).
5+ years of experience building and deploying ML models, with strong understanding and working knowledge of advanced statistical methods and Machine Learning techniques.
5+ years of experience in data manipulation on large datasets (ingestion, processing, merging and aggregation of data).
Strong programming skills in Python.
Proficiency in both SQL and Big data technologies (Hadoop, Spark).
Experience in automation and scheduling of data pipeline for end-to-end scoring solutions.
Excellent written and oral communication skills and the capability to drive cross-functional requirements with product and engineering teams.
The ability to present technical concepts and results in an audience-appropriate way.
An inquisitive and investigative nature: driven by curiosity and seeking answers to problems by sifting through data to discover patterns and customer journeys.
Ability to think outside of the box and work with ambiguity.

Nice To Haves

Advanced degree is an asset.
Experience working in cloud based modeling environment a plus (ie. AWS Sagemaker, AzureML, etc).

Responsibilities

Leverages advanced analytical algorithms and technologies (e.g. machine learning, deep learning, artificial intelligence) to mine and analyze large sets of structured and unstructured data to obtain insights.
Designs and constructs new processes for modeling data.
Develops predictive models and leverages big data technology to design solutions that deliver smarter business decisions, improve customer experience, and drive productivity.
Collaborates with other data and analytics professionals and teams to optimize, refine and scale machine learning solutions.
Manages end-to-end projects and interacts continuously with key stakeholders (strategy, implementation, business and model validation partners).
Mines data or extracts usable data from multiple data sources, processes, cleans, and validates the integrity of data to be used for analysis.
Analyzes large amounts of information to find patterns and solutions.
Develops prediction systems through the usage of machine learning tools and advanced statistics to select features, create and optimize classifiers or regression outcomes.
Presents results in a clear and concise manner for non-technical stakeholders and provides comprehensive model documentation.
Provides analysis and consultancy on model performance and recommends model usage.
Applies expertise and thinks creatively to address unique or ambiguous situations and to find solutions to problems that can be complex and non-routine.
Resolves issues raised by independent validation, Internal Audit and ongoing model monitoring.
Is accountable for existing models in production and responsible for documentation related to existing models (annual assessment and re-validation, model updates).