Data Scientist I – GenAI & Machine Learning

BlackCube Labs; Toronto; Hybrid; CAD 88,000 - 117,000; Full time

Job summary

BlackCube Labs is seeking a Data Scientist I to advance GenAI and ML initiatives in the insurance sector. You will translate business goals into analytical problems, develop ML products, and contribute to GenAI applications across Canadian business units.

Responsibilities include designing GenAI agents and chatbots, applying RAG methods, and working with LangChain/LangGraph. The role emphasizes feature engineering, model evaluation, and collaboration with limited stakeholder interaction.

Qualifications

• Bachelor’s degree or equivalent experience in a relevant field.
• 0-3 years of experience or recent graduates welcomed.
• Strong Python for data science and GenAI development.
• Experience with SQL and PySpark across multiple data sources.
• Proficiency in ML, including supervised/unsupervised methods.
• Experience with GenAI frameworks, LLM APIs, and prompt engineering.

Responsibilities

• Translate business goals into analytics problems and select algorithms or GenAI architectures.
• Support cross-functional teams in developing ML/data science products including GenAI apps.
• Apply data science techniques from design to implementation across projects.
• Focus on feature engineering, model training, evaluation, and prompt engineering for LLMs.
• Design, deploy GenAI solutions including AI agents, chatbots, and autonomous systems.
• Research GenAI pilots using RAG and agentic frameworks; tool-use patterns.
• Build intelligent retrieval systems with vector databases and embeddings.
• Support senior team members and create design artifacts for data models and interfaces.

Skills

Python

SQL

PySpark

Machine learning

Data transformation

APIs & microservices

GenAI frameworks

LLM APIs

Prompt engineering

Vector databases

Chatbot development

Security clearance

Education

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

Tools

AWS SageMaker

AWS Lambda

AWS Bedrock

AWS Glue

LangChain

LangGraph

CrewAI

Job description

Data Scientist I

GenAI & Machine Learning

Bring your passion for data, machine learning, and GenAI to the insurance sector for our client. Support the development of practical, innovative solutions while contributing to advanced analytics initiatives across Canadian business units.

What is in it for you:

• Salaried: $45-50 per hour.
• Incorporated Business Rate: $55-60 per hour.
• 12-month contract providing coverage for maternity leave.
• Full-time position: 37.50 hours per week.
• Hybrid work arrangement with 3 days on-site, including Thursdays.

Responsibilities:

Data Science and Machine Learning

• Support the translation of business goals into analytical problems and the identification of suitable algorithms, statistical techniques, and/or GenAI architecture.
• Support cross-functional teams in developing ML/data science products, including GenAI applications.
• Support the application of data science techniques, including descriptive, predictive, and machine learning methods, from design to implementation.
• Focus on feature engineering, model training, model evaluation, and prompt engineering for LLMs.

GenAI & Agentic AI Development

• Support the design, development, and deployment of GenAI solutions, including AI agents, chatbots, and autonomous systems.
• Support research and pilots involving GenAI technologies, RAG (Retrieval-Augmented Generation) techniques, and agentic frameworks such as LangChain, LangGraph, and CrewAI.
• Support the implementation of AI bots/agents with reasoning capabilities and tool-use patterns.
• Work with vector databases and embedding models to build intelligent information retrieval systems.

Technical Execution & Collaboration

• Support senior team members in completing data science development activities and work plans.
• Create and maintain technical design artifacts describing application functionality, data models, interfaces, and integrations.
• Work within a narrower project scope, typically one project at a time.
• Work with limited to no direct stakeholder interaction.
• Contribute to continuous improvement and innovation within the analytics community.

What you will need to succeed:

Required qualifications

• Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related field, or equivalent experience.
• Open to recent graduates and candidates with 0-3 years of relevant experience.
• Proficiency in Python for data science and GenAI application development.
• Experience writing complex SQL and PySpark queries to extract and integrate data from multiple database sources.
• Proficiency in machine learning, including supervised and unsupervised models.
• Demonstrated experience in data transformation, data manipulation, and working with structured and unstructured data.
• Strong understanding of APIs, microservices architecture, and cloud-native development.
• Experience with GenAI frameworks and LLM APIs, including developing AI bots and agents with reasoning and tool-use capabilities.
• Understanding of Retrieval-Augmented Generation techniques, prompt engineering, and fine-tuning methodologies, with familiarity with vector databases and embedding models.
• Experience with chatbot development concepts and conversational AI design.
• Reliability Clearance required.

Preferred qualifications

• Experience with AWS services, including SageMaker, Lambda, Bedrock, and other AI and machine learning services.
• Experience with data warehousing, pipelines, and big data technologies such as AWS Glue for ETL, Glue Catalog, Glue Data Quality, and AWS Step Functions.

Soft skills

• Exceptional communication skills.
• Effective and concise oral and written storytelling and insights communication skills.
• Strong problem-solving and analytical skills.
• Keen attention to detail.
• Fierce curiosity for discovering and leveraging new data.
• An inquisitive mind.
• A willingness to ask questions.
• A willingness to recognize that innovation attempts can lead to failure.
• A passion for solving problems.
• A focus on efficient execution and delivery.
• A thirst for continuously updating data science knowledge.