Senior Data Scientist — AI & ML Innovator (Hybrid Toronto)

Moneris; Toronto; Hybrid; CAD 128,000 - 154,000; Full time

Benefits offered by this job

Total Rewards Program

Flexible benefits starting day one

HSA or PSA

Job summary

Moneris in Toronto is seeking a Senior Data Scientist to lead impactful data science initiatives, develop and deploy advanced analytical models, and generate actionable insights to drive strategic decisions.

You will collaborate with engineering, product, and analytics teams, explore AI approaches such as Retrieval-Augmented Generation (RAG) and multimodal LLMs, and deploy models using Databricks and Snowflake in production.

Qualifications

• Advanced analytics experience and 5+ years in data science/Machine Learning.
• Hands-on experience deploying models to production environments.
• Strong programming and data governance practices.

Responsibilities

• Develop and deploy ML models addressing complex business problems.
• Apply statistical and ML techniques to forecast outcomes and inform decisions.
• Collaborate with engineering, product, and analytics for scalable solutions.
• Explore AI approaches like Retrieval-Augmented Generation (RAG) and multimodal LLMs.
• Work with Databricks and Snowflake using Python, PySpark, and SQL for data analysis and deployment.
• Evaluate new methodologies to enhance modeling capabilities.
• Partner with stakeholders to embed insights into workflows.

Skills

Python

SQL

Machine Learning

Data Analysis

Experimentation

Cross-functional Collaboration

Databricks

Snowflake

Git

CI/CD

Model Deployment

Education

Bachelor's or Master's in CS/Statistics/Engineering

Tools

Databricks

Snowflake

Python

SQL

Git

CI/CD pipelines

Job description

Moneris in Toronto is seeking a Senior Data Scientist to lead impactful data science initiatives, develop and deploy advanced analytical models, and generate actionable insights to drive strategic decisions.

You will collaborate with engineering, product, and analytics teams, explore AI approaches such as Retrieval-Augmented Generation (RAG) and multimodal LLMs, and deploy models using Databricks and Snowflake in production.