GenAI & Data Scientist I - Hybrid Toronto

Raise; Toronto; Hybride; CAD 68 000 - 80 000; Plein temps

Résumé du poste

Raise is hiring a Data Scientist I for one of our top clients in Toronto. You will translate business goals into analytical problems, identify algorithms, and work in cross‑functional teams to develop ML and GenAI products.

You will apply a range of data science techniques from design to deployment, with emphasis on feature engineering and prompt engineering for LLMs. The role covers GenAI development, AI agents, vector databases, and RAG techniques, requiring strong communication and mentoring

Qualifications

• Bachelor’s degree or equivalent experience in CS/Statistics/Math.
• 5+ years’ experience developing and implementing data science techniques.
• Proficient in Python for data science and GenAI development.
• Experience with complex SQL and PySpark queries from multiple sources.
• ML knowledge across supervised/unsupervised models and data transformation.

Responsabilités

• Translate business goals into analytical problems and select appropriate algorithms.
• Develop ML/GenAI products in cross‑functional teams, including GenAI apps.
• Apply descriptive, predictive, and ML methods from design to implementation.
• Focus on feature engineering, model training, evaluation, and prompt engineering for LLMs.

Connaissances

Python for data science

SQL

PySpark

Communication

Mentoring

Problem solving

Formation

Bachelor’s degree in Computer Science, Statistics, Mathematics, or related field

Outils

Python

LangChain

LangGraph

CrewAI

Vector Databases

LLM APIs

Description du poste

Raise is hiring a Data Scientist I for one of our top clients in Toronto. You will translate business goals into analytical problems, identify algorithms, and work in cross‑functional teams to develop ML and GenAI products.

You will apply a range of data science techniques from design to deployment, with emphasis on feature engineering and prompt engineering for LLMs. The role covers GenAI development, AI agents, vector databases, and RAG techniques, requiring strong communication and mentoring