AI Data Science Engineer
- Toronto, ON, Canada
$70 to $90 CAD / hour · Contract
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Job details
- Pay
- $70 to $90 CAD / hour
- Job type
- Contract
- Apply by
- Experience
- 2+ years of hands-on Data Science, Machine Learning, or AI Engineering experience.
- Posting language
- English
- Posted
Original job description
JOB DESCRIPTION
We are seeking an AI Data Science Engineer to join a team focused on developing and productionizing advanced AI and machine learning solutions for Commercial Banking and underwriting.
This is a hybrid Data Scientist + AI/ML Engineer role. The ideal candidate can take an ambiguous business problem, translate it into a mathematical/data science problem, build the appropriate model, and then help bring that solution into production.
Key Responsibilities
Design, build, and productionize AI and machine learning models.
Develop AI agents and agentic workflows using modern LLM frameworks.
Build solutions supporting commercial underwriting and prospect scouting.
Apply traditional machine learning, deep learning, and graph-based modeling techniques to complex business problems.
Develop and optimize models using Python and PySpark.
Work with graph neural networks / graph-based machine learning where appropriate.
Build agentic AI solutions using LangChain and LangGraph within AWS environments.
Take high-level or abstract business concepts and translate them into mathematical, analytical, and technical solutions.
Partner with business and technical stakeholders to move AI solutions from concept → model development → production.
Evaluate model performance and continuously improve AI/ML solutions.
REQUIRED SKILLS AND EXPERIENCE
2+ years of hands-on Data Science, Machine Learning, or AI Engineering experience.
Strong hands-on Python development experience.
Experience building machine learning models from scratch.
Strong understanding of traditional ML methodologies.
Experience with deep learning.
Hands-on experience building and productionizing AI/ML models.
Experience with PySpark for large-scale data processing.
Experience with scikit-learn or comparable Python ML libraries.
Experience building AI agents / agentic AI solutions.
Strong experience with LangChain and/or LangGraph.
Experience developing AI/ML solutions within AWS.
Strong problem-solving and critical-thinking skills, especially the ability to translate an abstract business problem into a structured mathematical or ML problem.
NICE TO HAVE SKILLS AND EXPERIENCE
Banking experience
Artificial intelligence tools are used to assist in screening and assessing applicants for this position.
Vacancy: This posting is for a currently vacant role, and the successful candidate will be hired into an existing open position.
70-90/hr
More about this job
Responsibilities
Design, develop, and productionize AI and machine learning models, agents, and workflows for commercial underwriting and prospect scouting, using techniques such as traditional ML, deep learning, and graph-based modeling. Translate business problems into analytical and technical solutions, collaborate with stakeholders to deliver them to production, and evaluate and improve model performance.
Requirements
Requires at least two years of hands-on experience in data science, machine learning, or AI engineering, strong Python skills, and experience building and productionizing models using traditional ML, deep learning, and PySpark. Candidates should also have experience with AI agents, LangChain or LangGraph, AWS, and translating abstract business problems into structured mathematical or machine learning problems; banking experience is a plus.
Skills
- Python
- PySpark
- Machine Learning
- Deep Learning
- Artificial Intelligence
- AI/ML Model Productionization
- AI Agents
- Agentic Workflows
- LangChain
- LangGraph
- AWS
- Scikit-Learn
- Graph-Based Machine Learning
- Graph Neural Networks
- Data Science
- Commercial Underwriting
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Technology
- Software
- Finance & Accounting
- Science & Research
Keywords
- Artificial Intelligence
- Machine Learning
- Data Science
- AI Engineering
- Python
- PySpark
- Scikit-Learn
- Deep Learning
- Traditional Machine Learning
- Graph-Based Modeling
- Graph Neural Networks
- AI Agents
- Agentic Workflows
- LangChain
- LangGraph
- AWS
- Model Productionization
- Commercial Banking
- Commercial Underwriting
- Prospect Scouting
- Large-Scale Data Processing
- Model Performance Evaluation
- Mathematical Modeling
- Stakeholder Collaboration