Data Science Analyst

Role Overview

We are seeking a Data Science Analyst for a hybrid contract position based in Mississauga, Ontario. This role involves working three days on-site weekly and requires advanced skills in data analysis and machine learning.

Key Responsibilities

• Analyze extensive structured and unstructured datasets to extract trends, patterns, and actionable business insights.
• Perform data cleansing, transformation, and feature engineering to facilitate model creation.
• Design, test, and sustain predictive and prescriptive models utilizing statistical and machine learning methodologies.
• Collaborate with technology teams to deploy analytical solutions into production systems.
• Participate in machine learning lifecycle activities including development, testing, training, monitoring, and evaluation of model performance.
• Work closely with business, technology, and risk teams to understand needs and translate them into practical analytical solutions.
• Document methodologies, assumptions, and model outcomes to support governance and oversight.
• Communicate analytical results and project progress to colleagues and stakeholders.
• Stay updated on and apply new advancements in Machine Learning, Deep Learning, Large Language Models, and Generative AI.

Required Skills and Qualifications

• Master's or Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative discipline.
• Minimum of 5 years of professional experience in data science, machine learning, or advanced analytics.
• Proficient in developing and assessing machine learning models.
• Solid understanding of machine learning and deep learning techniques and the full model development lifecycle.
• Skilled in Python, SQL, Spark, PySpark, TensorFlow, or comparable analytical and modeling tools.
• Experience or familiarity with Large Language Models and Generative AI technologies.
• Excellent analytical, problem-solving, and communication abilities.
• Capable of working both independently and collaboratively within multidisciplinary teams.

Preferred Experience

• Background supporting machine learning, AI, or Generative AI projects in production environments.
• Experience with distributed computing and data platforms such as Hadoop, Hive, Spark, or cloud-based analytics services.
• Exposure to banking, retail risk management, or financial services sectors.
• Basic knowledge of capital markets, financial instruments, and quantitative modeling principles.

Education

Requires at least a Bachelor's degree or equivalent in a STEM field, with advanced degrees being advantageous.