Front Office Data Scientist: Quant Analytics

Bank of Montreal

Toronto

On-site

CAD 90,000 - 110,000

Full time

14 days+

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Benefits offered by this job

Health insurance

Tuition reimbursement

Retirement savings plans

Accident and life insurance

Job summary

BMO Capital Markets is seeking a skilled Data Scientist to join the Data Cognition Team. You will develop advanced analytics and ML models to support algorithmic trading strategies and business decisions across front office, Investment Banking, and Corporate Banking.

You will collaborate with trading desks and business units, build scalable ML pipelines, and communicate insights to senior leadership. A strong foundation in quantitative methods and programming will help you advance our trading

Qualifications

Background in computer science, mathematics, physics, or a related quantitative field.
Minimum 2 years of working experience in data science or machine learning or equivalent master in these fields.
Proficiency in statistical modeling, time series analysis, and machine learning frameworks.
Strong programming skills (Python preferred) and experience with cloud technologies.
Familiarity with financial tools such as Bloomberg, FactSet, and Thomson Reuters is a plus.
Excellent problem-solving skills and ability to formulate hypotheses and learn from data.
Strong communication skills for effective collaboration with technical and business stakeholders.

Responsibilities

Develop and implement advanced analytics and machine learning models using Generative AI, Deep Learning, and Reinforcement Learning.
Build predictive models for algorithmic trading across asset classes including Equities, Fixed Income, Commodities, and Forex.
Collaborate with trading desks, investment banking teams, and corporate banking stakeholders to identify opportunities and optimize strategies.
Research and experiment with new machine learning techniques, including time series analysis, to improve trading performance and profitability.
Design and maintain reproducible machine learning pipelines for production environments.
Analyze large datasets to extract insights and support strategic decision-making.
Communicate complex technical concepts to non-technical stakeholders and present findings to senior management.
Visualize complex data clearly and concisely for business consumption.