Data Science Analyst (Python, PySpark & Machine Learning)

Grow your career as a Data Science Analyst with an innovative global bank in Mississauga, ON with strong possibility of extension or conversion to full-time employment. Will require working onsite 3 days per week.

Join one of the world's most renowned global banks and trusted brand with over 200 years of continuously evolving financial services worldwide. You will work alongside some of the smartest minds in the industry who are excited to share their knowledge and to learn from you.

Contract Duration: 6+ Months

• Required Skills & Experience Master's degree or Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field. Advanced degree preferred.
• 5+ years of experience in data science, machine learning, advanced analytics, or a related field.
• Experience developing and evaluating machine learning models.
• Working knowledge of ML/DL techniques and model development processes.
• Proficiency in Python, SQL, Spark, PySpark, TensorFlow, or similar analytical and model-building tools.
• Familiarity with LLMs and GenAI technologies.
• Strong analytical, problem-solving, and communication skills.
• Ability to work independently while collaborating effectively within cross-functional teams.

• Preferred Skills Experience supporting ML, AI, or GenAI initiatives in a production environment.
• Familiarity with distributed data and computing platforms such as Hadoop, Hive, Spark, or cloud-based analytics platforms.
• Exposure to banking, Retail Risk management, or financial services.
• Basic understanding of capital markets, financial instruments, and quantitative modeling concepts.

What You Will Be Doing

• Analyze large structured and unstructured datasets to identify trends, patterns, and business insights.

• Perform data cleansing, transformation, and feature engineering to support model development.

• Develop, test, and maintain predictive and prescriptive models using statistical and machine learning techniques.

• Support the deployment of analytical solutions into production environments in partnership with technology teams.

• Contribute to the implementation of machine learning lifecycle processes, including development, testing, training, monitoring, and performance evaluation.

• Collaborate with business, technology, and risk partners to understand requirements and translate them into analytical solutions.

• Document methodologies, assumptions, and model results to support governance and review processes.

• Present analytical findings and project updates to team members and stakeholders.

• Continuously learn and apply emerging techniques in Machine Learning, Deep Learning, Large Language Models (LLMs), and Generative AI.