Data Scientist, Associate

PwC•Toronto, ON

23h•CA$58,400 - CA$97,500

About The Position

At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in artificial intelligence engineering at PwC will apply data, algorithms, and software engineering to build and deploy software and platform systems that create Artificial Intelligence and Machine Learning based solutions at scale. Your work will involve designing AI systems, data wrangling, and software implementation to enable the AI models to be useful and scalable.

Requirements

1-2 years of experience in either theoretical or applied use of machine learning, with a proven track record of delivering data-driven solutions in a research or business environment.
Strong problem-solving abilities and a keen analytical mindset.
Ability to convey technical concepts clearly to non-technical stakeholders.
Understanding of business processes and the ability to translate business problems into data science solutions.
Ability to work collaboratively in a team-oriented environment.
Proficiency in programming languages such as Python, R, or Scala.
Strong experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
Experience in data manipulation and analysis using SQL and data visualization tools (e.g., Tableau, Power BI).
Hands-on experience with data science services in Cloud platforms (AWS, Azure, GCP).
Ability to implement MLOps using common frameworks such as MLflow.
Post secondary (Bachelor) degree in a quantitative or relevant business subject such as computer science, data science, statistics, mathematics, economics, finance, engineering or a related field

Nice To Haves

Familiarity with big data technologies such as Hadoop, Spark, or Kafka is a plus.

Responsibilities

Develop and deploy machine learning models (predictive modeling, forecasting, segmentation, propensity scoring, etc.) to address complex business challenges that meet key performance metrics and requirements.
Build and deploy simulation and optimization solutions
Work on both theoretical and practical applications of both traditional and modern AI problems across advanced statistical, machine learning, deep learning and LLM based solutions.
Design and build AI agents and Agentic solutions to solve critical client problems.
Implement best practices in data science workflows and identify areas for process improvement and automation.
Translate data-driven insights into actionable business recommendations and strategic initiatives, facilitating seamless integration into business operations through collaboration with cross-functional teams.
Integrate machine learning solutions into production systems using MLOps frameworks, focusing on process improvement and automation.
Help clients develop their data and AI strategy program and subsequent initiatives to leverage data and analytics to meet their organizational goals.
Define and measure AI and analytics capabilities that span enterprise AI and data strategy, as well as lifecycle from data, technology, process, governance and talent to organizational structure and data-culture.
Identify value creation opportunities, conducting jurisdictional scans, documenting case studies and leading practices as it relates to enterprise applications of AI, advanced analytics, strategy and governance.