AI Data Science Engineer
- Toronto, ON, Canada
$80 to $94 CAD / hour · Contract
Applications open in a new tab.
Job details
- Pay
- $80 to $94 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
12 month contact + ext
Hybrid 3x per week in Toronto
Pay: $80-94/hr incorp
Must-Have Skills
- 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 & Experience
Banking experience
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.
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.
More about this job
Responsibilities
Design, develop, evaluate, and productionize AI and machine learning models and agentic workflows for commercial underwriting and prospect scouting. Translate ambiguous business needs into analytical and technical solutions, collaborating with business and technical stakeholders to bring models from concept into production and improve their performance.
Requirements
Requires at least two years of hands-on data science, machine learning, or AI engineering experience, strong Python skills, and experience building models from scratch and productionizing them. Candidates should have experience with traditional and deep learning, PySpark, scikit-learn or comparable libraries, AI agents using LangChain or LangGraph, and AWS; banking experience is preferred.
Skills
- Python
- Machine Learning
- Deep Learning
- AI Engineering
- PySpark
- Scikit-Learn
- AI Agents
- Agentic AI
- LangChain
- LangGraph
- AWS
- Graph Neural Networks
- Graph-Based Machine Learning
- Model Productionization
- Problem-Solving
- Critical Thinking
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Technology
- Software
- Science & Research
- Finance & Accounting
Keywords
- Artificial Intelligence
- Data Science
- Machine Learning
- AI Engineering
- Python
- PySpark
- Scikit-Learn
- Deep Learning
- Traditional Machine Learning
- AI Agents
- Agentic AI
- LangChain
- LangGraph
- AWS
- Graph Neural Networks
- Graph-Based Modeling
- Model Development
- Model Productionization
- Commercial Banking
- Commercial Underwriting
- Prospect Scouting
- Large-Scale Data Processing
- Model Evaluation
- Mathematical Modeling
- Business Problem Translation
- Stakeholder Collaboration
- Banking