Our public sector client is seeking a Data Scientist to develop machine learning and generative/agentic AI solutions using Python, R, Spark, and cloud platforms

S.I. Systems Ltd.; Vancouver; Hybrid; CAD 90,000 - 130,000; Full time

Job summary

S.I. Systems Ltd. seeks a Data Scientist to develop machine learning and generative/agentic AI solutions using Python, R, Spark, and cloud platforms.

You will join a BI & Data Science team focused on safety oversight, supporting development, evaluation, and deployment of AI solutions across inspection, compliance, and audit processes. The role handles complex data sources with cross-functional teams to deliver scalable analytics and AI products.

Qualifications

• Master's Degree or above in Computer Science, Statistics, or Data Science.
• Proven experience building ML and generative AI solutions.
• Experience with Python and R for data science.

Responsibilities

• Develop machine learning and generative/agentic AI products through team collaboration.
• Analyze safety data to support inspection, certificate, audit, compliance, and enforcement activities.
• Build scalable machine learning and AI solutions, including retrieval-augmented generation and AI-assisted workflows.
• Design, evaluate, implement, and test machine learning models and generative/agentic AI solutions for production deployment.
• Work with multiple complex data sources, including social media APIs and open source APIs.
• Collaborate with Business Intelligence analysts on BI development projects.
• Identify new data sources and patterns that improve machine learning model performance.

Skills

machine learning

generative AI

Python

R

Spark

SQL

NoSQL

cloud platforms

Education

Master's degree or above in Computer Science, Statistics, or Data Science

Tools

AWS

Azure

Job description

Our public sector client is seeking a Data Scientist to develop machine learning and generative/agentic AI solutions using Python, R, Spark, and cloud platforms

Our public sector client is seeking a Data Scientist to develop machine learning and generative/agentic AI solutions using Python, R, Spark, and cloud platforms

Join a Business Intelligence & Data Science team focused on improving safety oversight through data science and AI initiatives. This role supports the development, evaluation, and deployment of machine learning and generative/agentic AI solutions across inspection, compliance, and audit processes. The position works with complex data sources and cross-functional teams to deliver scalable analytics and AI products. This contract opportunity provides critical coverage for an ongoing data science function within the organization.

Must Haves

• Strong background applying machine learning techniques and generative AI solutions
• Strong ability with Python and R
• Fluency with Spark, relational SQL and NoSQL databases, and cloud platforms such as AWS or Azure
• Experience building and optimizing data pipelines, architectures, and datasets
• Master's Degree or above in Computer Science, Statistics, or Data Science

Nice to Have

• Experience with natural language processing, large language models, and/or computer vision
• Experience with agile project management
• Experience prototyping deployment tools such as web app development
• Experience working with social media APIs and open source APIs

Responsibilities

• Develop machine learning and generative/agentic AI products through team collaboration
• Analyze safety data to support inspection, certificate, audit, compliance, and enforcement activities
• Build scalable machine learning and AI solutions, including retrieval-augmented generation and AI-assisted workflows
• Design, evaluate, implement, and test machine learning models and generative/agentic AI solutions for production deployment
• Work with multiple complex data sources, including social media APIs and open source APIs
• Collaborate with Business Intelligence analysts on BI development projects
• Identify new data sources and patterns that improve machine learning model performance

Our public sector client is seeking a Data Scientist to develop machine learning and generative/agentic AI solutions using Python, R, Spark, and cloud platforms

Join a Business Intelligence & Data Science team focused on improving safety oversight through data science and AI initiatives. This role supports the development, evaluation, and deployment of machine learning and generative/agentic AI solutions across inspection, compliance, and audit processes. The position works with complex data sources and cross-functional teams to deliver scalable analytics and AI products. This contract opportunity provides critical coverage for an ongoing data science function within the organization.

Contract, Vancouver, Hybrid, Working Hours: PST

Must Haves

• Strong background applying machine learning techniques and generative AI solutions
• Strong ability with Python and R
• Fluency with Spark, relational SQL and NoSQL databases, and cloud platforms such as AWS or Azure
• Experience building and optimizing data pipelines, architectures, and datasets
• Master's Degree or above in Computer Science, Statistics, or Data Science

Nice to Have

• Experience with natural language processing, large language models, and/or computer vision
• Experience with agile project management
• Experience prototyping deployment tools such as web app development
• Experience working with social media APIs and open source APIs

Responsibilities

• Develop machine learning and generative/agentic AI products through team collaboration
• Analyze safety data to support inspection, certificate, audit, compliance, and enforcement activities
• Build scalable machine learning and AI solutions, including retrieval-augmented generation and AI-assisted workflows
• Design, evaluate, implement, and test machine learning models and generative/agentic AI solutions for production deployment
• Work with multiple complex data sources, including social media APIs and open source APIs
• Collaborate with Business Intelligence analysts on BI development projects
• Identify new data sources and patterns that improve machine learning model performance