Data Scientist (Machine Learning) with Pandas, Numpy & Scipy experience to perform feature engineering, data manipulation and build ML Models for our Tier1 Banking client

Data Scientist (Machine Learning) with Pandas, NumPy & SciPy experience to perform feature engineering, data manipulation and build ML Models for our Tier1 Banking client

Duration: 12 months

Location: Toronto (Hybrid)

Overview

Our Major Banking Client is seeking three experienced Data Scientists to support enterprise analytics and machine learning initiatives. These roles will be responsible for delivering end-to-end data science solutions—from business requirement gathering through to model development, validation, and production deployment. The ideal candidates will combine strong technical expertise with excellent communication skills to partner effectively with business stakeholders, data engineers, and MLOps teams.

Key Responsibilities

Partner directly with business stakeholders to gather requirements, define use cases, and translate business needs into analytical solutions. Collaborate with Data Engineering teams to support data extraction, transformation, and loading (ETL) processes. Perform in-depth exploratory data analysis, including data profiling, cleansing, manipulation, and feature engineering. Develop, train, and optimize traditional machine learning models such as classification, regression, and clustering algorithms. Conduct model validation, testing, and performance evaluation to ensure accuracy, robustness, and business relevance. Prepare comprehensive model documentation, including methodology, assumptions, and technical specifications. Work closely with MLOps teams to deploy models into production environments and support ongoing monitoring. Utilize Python and SQL as primary programming languages for data processing and model development. Communicate analytical findings and model outcomes effectively to both technical and non-technical audiences.

Must Have Skills & Experience

5+ years of professional experience as a Data Scientist within large enterprise environments. Strong hands-on experience with end-to-end machine learning workflows, from requirements gathering to production deployment. Advanced proficiency in Python, including libraries such as Pandas, NumPy, SciPy, and common machine learning frameworks. Solid expertise in SQL for data extraction, transformation, and analysis. Demonstrated experience building traditional machine learning models (classification, regression, clustering). Proven experience conducting exploratory data analysis and feature engineering. Experience working closely with Data Engineering teams on ETL processes. Strong communication skills with the ability to engage effectively with business stakeholders. Experience validating, testing, and documenting machine learning models. Familiarity with model deployment processes and collaboration with MLOps teams.

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