Machine Learning Engineer

Position Title: Machine Learning Professional / Data Scientist

Duration: 12 Months (Possible Extension)

Location: Toronto, ON (Hybrid – 2–3 days onsite per week)

Position Overview

We are seeking an intermediate-level AI/ML Professional / Data Scientist to support end-to-end delivery of predictive, quantitative machine learning solutions. This role focuses on traditional machine learning and statistical modeling, not GenAI, LLMs, or NLP-heavy use cases.

The ideal candidate will work closely with business stakeholders, data engineers, and MLOps teams to translate business requirements into scalable, production-ready ML models that drive data-driven decision making.

Important Notes

• This role is not focused on GenAI, LLMs, or prompt engineering
• The emphasis is on predictive, quantitative, and statistical machine learning models
• Candidates may come from any industry; banking experience is a plus but not required

Key Responsibilities

• Partner with business stakeholders to gather requirements and translate business problems into analytical and machine learning solutions
• Collaborate with data engineers on data ingestion, ETL pipelines, and preparation of large-scale datasets
• Perform exploratory data analysis (EDA), feature engineering, data manipulation, and preprocessing using Python libraries such as Pandas, NumPy, and SciPy
• Design, develop, and implement traditional machine learning models including classification, regression, clustering, and other predictive techniques
• Validate, test, and evaluate model performance to ensure accuracy, stability, and business relevance
• Document models, assumptions, methodologies, and results for technical and non-technical audiences
• Work with MLOps and cloud teams to deploy, monitor, and maintain models in production
• Analyze large and complex datasets to identify patterns, trends, and actionable insights
• Provide data-driven recommendations to support strategic and operational decision making
• Contribute to continuous improvement of analytics processes, tools, and best practices

Required Skills & Experience

• 2+ years of experience building and deploying machine learning models in a production environment
• 2+ years of experience working with large datasets, including data ingestion, processing, merging, and aggregation
• Strong programming skills in Python and SQL (SAS is an asset)
• Solid understanding of statistics, mathematics, and quantitative modeling techniques
• Hands-on experience with data wrangling, preprocessing, and feature engineering
• Strong problem-solving, analytical, and critical-thinking skills
• Excellent verbal and written communication skills, with the ability to engage business stakeholders
• Ability to work independently and manage non-routine analytical problems

Preferred / Nice-to-Have Qualifications

• Experience with pricing or revenue modeling
• Exposure to Banking, Insurance, or Financial Services domains (not mandatory)
• Experience with big data platforms and data visualization tools
• Familiarity with cloud platforms and MLOps workflows (e.g., model deployment and monitoring)
• Experience with Python-based ML frameworks and cloud services (e.g., SageMaker is an asset)
• Knowledge of trust, bias, ethics, and responsible AI practices