GenAI Data Scientist I — Hybrid (Toronto)

Ian Martin Group; Toronto; Hybrid; CAD 68,000 - 80,000; Full time

Job summary

Raise is hiring a Data Scientist I for a top client in Toronto. The role is hybrid (2/3 on-site) and a 12-month contract with a pay rate of $48.79–$57.59/hr.

You translate business goals into analytical problems, develop ML and GenAI products, and collaborate across functions to deploy AI solutions, including agents and chatbots, using modern tools and best practices. The role emphasizes feature engineering, model training, evaluation, prompt engineering for LLMs, and building retrieval systems

Qualifications

• Bachelor’s degree in computer science, statistics, mathematics, or related field, or equivalent experience.
• 5+ years’ experience developing and implementing data science techniques.
• Proficient in Python for data science and GenAI application development.
• Experience with writing complex SQL and PySpark queries to extract and integrate data from multiple database sources.
• Proficiency in machine learning including supervised and unsupervised models.
• Experience with data transformation and working with structured vs. unstructured data.
• Strong understanding of APIs, microservices architecture, and cloud-native development.
• Hands‑on expertise with GenAI frameworks and LLM APIs with experience developing AI bots/agents with reasoning and tool‑use capabilities.
• Strong understanding of RAG techniques, prompt engineering, and fine‑tuning methodologies and familiarity with vector databases and embedding models.
• Experience with chatbot development concepts and conversational AI design.
• Exceptional communication skills to articulate complex technical concepts to both technical and non-technical stakeholders.
• Effective and concise oral and written storytelling and insights communication skills.
• Ability to work on multiple projects in parallel while managing changing deadlines and priorities.
• Proven ability to mentor and guide junior data scientists and data engineers.
• Strong problem‑solving abilities and analytical skills with keen attention to detail.

Responsibilities

• Translate business goals into analytical problems; identify optimal algorithms and GenAI architecture for the business problem.
• Work in cross-functional teams to develop ML/data science products, including GenAI applications.
• Apply best-in-breed data science techniques including descriptive, predictive, and ML methods from design to implementation.
• Focus on feature engineering, model training, evaluation, and prompt engineering for LLMs.
• Design, develop, and deploy GenAI solutions including AI agents, chatbots, and autonomous systems.
• Research and pilot latest GenAI technologies, RAG techniques, and agentic frameworks.
• Implement AI bots/agents with advanced reasoning capabilities and tool use patterns.
• Work with vector databases and embedding models to build intelligent information retrieval systems.
• Break down milestones into actionable goals; create technical design artifacts describing functionality, data models, interfaces, and integrations.
• Engage stakeholders and present findings to multiple levels; champion continuous improvement and innovation.

Skills

Python for data science

GenAI development

SQL

PySpark

ML algorithms

RAG techniques

LLM APIs

Vector databases

API & microservices

Cloud-native development

Communication

Mentoring juniors

Education

Bachelor’s degree in CS/Statistics/Math or related field

Tools

AWS SageMaker

AWS Lambda

AWS Bedrock

ETL / Data warehousing

Glue

Job description

Raise is hiring a Data Scientist I for a top client in Toronto. The role is hybrid (2/3 on-site) and a 12-month contract with a pay rate of $48.79–$57.59/hr.

You translate business goals into analytical problems, develop ML and GenAI products, and collaborate across functions to deploy AI solutions, including agents and chatbots, using modern tools and best practices. The role emphasizes feature engineering, model training, evaluation, prompt engineering for LLMs, and building retrieval systems