Data Scientist – Traditional Machine Learning (Classification-Focused) for our Tier1 Banking client

Data Scientist – Traditional Machine Learning (Classification-Focused) for our Tier1 Banking client

Duration: 12 months

Location: Toronto (Hybrid)

Overview

Our Major Banking Client is seeking experienced Data Scientists to deliver hands-on, traditional machine learning solutions with a strong emphasis on exploratory data analysis (EDA), feature engineering, and classification modeling.

This role is not focused on GenAI, LLMs, NLP, or ML platform engineering. Instead, it requires practitioners who have repeatedly built, validated, and productionized classical ML models in enterprise environments and can work closely with business stakeholders to solve structured analytical problems.

Key Responsibilities

Lead hands-on exploratory data analysis, including data profiling, cleansing, transformation, and feature engineering. Translate business requirements into well-defined ML problem statements, with a clear focus on supervised learning use cases. Build, tune, and validate traditional ML models, with a strong emphasis on classification (regression and clustering as secondary). Select appropriate features, algorithms, and evaluation metrics aligned to the business objective. Perform model testing, cross-validation, and performance analysis; clearly articulate trade-offs and limitations. Produce clear and complete model documentation covering methodology, assumptions, and results. Collaborate with Data Engineers on data readiness and ETL (not a data engineering or platform role). Partner with MLOps teams to support model deployment and monitoring (not an ML engineering role). Communicate analytical insights and model outcomes effectively to business stakeholders.

Must Have Skills & Experience

5+ years of hands-on Data Science experience focused on traditional machine learning. Demonstrated experience building classification models end-to-end (strongly preferred). Deep practical experience with EDA and feature engineering using Python (Pandas, NumPy, SciPy). Strong experience with classical ML algorithms (e.g., logistic regression, decision trees, random forests, gradient boosting, k-means). Solid proficiency in SQL for data extraction and analysis. Experience validating and testing models using appropriate statistical and ML metrics. Proven ability to document models and analytical approaches for enterprise use. Strong communication skills with experience engaging business stakeholders directly.

Nice to Have

Experience within the financial services or banking industry Exposure to cloud platforms such as AWS, Azure, or GCP Familiarity with ML lifecycle tools and frameworks Experience working in Agile project environments Knowledge of advanced analytics, A/B testing, or optimization techniques