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# Data Scientist

- **Company:** BMO Harris Bank National Association
- **Location:** Toronto, OH, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Big Data, Computer Engineering, Data Cleansing, Data Systems, Data Visualization, Decision Support Systems, Python (Programming Language), Machine Learning, NumPy, SciPy, SQL Databases, Unstructured Data, Data Processing, Feature Engineering, Data Strategy, Pandas, Scikit Learn, Information Technology, Data Analytics
- **Published:** August 6, 2026
- **Apply:** https://us.experteer.com/career/view-jobs/data-scientist-toronto-oh-usa-58815031

## About the Role

large decision making within the Risk Management Framework * Support non-routine analysis and consulting across the business group * Contribute to cross-functional data strategy and future roadmap * Balance focus between the specific BMO business group and broader enterprise initiatives Tasks * 5-7 years of relevant experience * Post-secondary degree in Computer Science, Computer Engineering or related field, or equivalent experience * Fluent in SQL and Python * Strong knowledge of machine learning algorithms * Experience in Exploratory Data Analysis, feature engineering, and data manipulation (Pandas, NumPy, SciPy) * Experience building traditional ML models (classification, regression, clustering) with scikit-learn * Big data, data wrangling, data preprocessing and data visualization * Strong analytical, problem-solving, verbal and written communication skills * Collaboration, team orientation, and ability to influence stakeholders * Ability to work independently and handle aaaaaaaaao
_ situations * Familiarity with risk management concepts and ethical considerations Key requirements * health insurance * tuition reimbursement * accident and life insurance * retirement savings plans * performance-based incentives * discretionary bonuses

## Description

Experteer Overview In this role you will advance data-driven decision making at BMO by building and deploying predictive models on large data sets. You will work with data engineers and cross-functional teams to scale analytics into mature solutions and influence business strategy. You will explore new AI/ML approaches to improve customer experience and productivity while adhering to risk governance. This contract offers the chance to shape data strategy and demonstrate impact on a leading financial institution. Compensation / Benefits * Mine and analyze large structured and unstructured data to extract insights * Design and construct data modeling processes and predictive models * Collaborate with data engineers and product teams to operationalize analytics * Evaluate feasibility of AI/ML solutions and build scalable data solutions * Provide data-driven recommendations to business leaders and stakeholders * Explore new technologies to develop innovative data solutions * Apply risk-aware decision making within the Risk Management Framework * Support non-routine analysis and consulting across the business group * Contribute to cross-functional data strategy and future roadmap * Balance focus between the specific BMO business group and broader enterprise initiatives Tasks * 5-7 years of relevant experience * Post-secondary degree in Computer Science, Computer Engineering or related field, or equivalent experience * Fluent in SQL and Python * Strong knowledge of machine learning algorithms * Experience in Exploratory Data Analysis, feature engineering, and data manipulation (Pandas, NumPy, SciPy) * Experience building traditional ML models (classification, regression, clustering) with scikit-learn * Big data, data wrangling, data preprocessing and data visualization * Strong analytical, problem-solving, verbal and written communication skills * Collaboration, team orientation, and ability to influence stakeholders * Ability to work independently and handle non-routine situations * Familiarity with risk management concepts and ethical considerations Key requirements * health insurance * tuition reimbursement * accident and life insurance * retirement savings plans * performance-based incentives * discretionary bonuses


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