Data Scientist

Role Overview

This position is for a Data Scientist based in Mississauga, ON, requiring onsite presence three days per week on a contract basis.

Key Responsibilities

• Examine substantial structured and unstructured datasets to uncover trends, patterns, and valuable business insights.
• Perform tasks such as data cleansing, transformation, and feature engineering to facilitate model creation.
• Create, evaluate, and sustain both predictive and prescriptive models using statistical and machine learning approaches.
• Assist in deploying analytical solutions in production settings in collaboration with technology teams.
• Support the machine learning lifecycle including development, testing, training, monitoring, and performance assessment.
• Work jointly with business, technology, and risk stakeholders to gather requirements and convert them into analytical outputs.
• Maintain documentation covering methodologies, assumptions, and modeling outcomes to aid governance and assessment processes.
• Deliver presentations on analytical results and project progress to colleagues and stakeholders.
• Stay current with and apply advanced methods in Machine Learning, Deep Learning, Large Language Models (LLMs), and Generative AI.

Preferred Qualifications

• A minimum of five years in data science, machine learning, advanced analytics, or related disciplines.
• Experience in creating and assessing machine learning models.
• Familiarity with ML/DL concepts and the model development lifecycle.
• Proficiency in Python, SQL, Spark, PySpark, TensorFlow, or comparable analytic and modeling software.
• Knowledge of Large Language Models and Generative AI tools.
• Strong problem-solving, analytical capabilities, and effective communication skills.
• Capability to work autonomously while collaborating productively within cross-functional teams.

Additional Desired Skills

• Hands-on experience supporting ML, AI, or GenAI applications in production environments.
• Working knowledge of distributed computing frameworks such as Hadoop, Hive, Spark, or cloud analytics platforms.
• Background in banking, retail risk management, or financial services is advantageous.
• Basic familiarity with capital markets, financial instruments, and quantitative modeling.

Educational Requirements

A bachelor's or university degree in a STEM-related field is required, with advanced degrees preferred.