Senior Data Scientist (Machine Learning & Generative Ai)

Senior Data Scientist (Machine Learning & Generative AI)Bring your passion for data, machine learning, and GenAI to the insurance sector for our client. Help transform complex business problems into practical, innovative solutions while contributing to advanced analytics initiatives that create value across Canadian business units.
What is in it for you:
Salaried: $65-75 per hour.
Incorporated Business Rate: $77-87 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
Translate business goals into analytical problems and identify optimal algorithms, statistical techniques, and/or GenAI architecture suitable for the business problem at hand.
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 machine learning methods, from design to implementation.
Focus on feature engineering, model training, model evaluation, and prompt engineering for LLMs.
GenAI & Agentic AI Development
Design, develop, and deploy GenAI solutions, including AI agents, chatbots, and autonomous systems that solve complex business problems.
Research and pilot the latest GenAI technologies, RAG (Retrieval-Augmented Generation) techniques, and agentic frameworks such as LangChain, LangGraph, and CrewAI.
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.
Technical Leadership & Collaboration
Break down broader data science development milestones into actionable goals, activities, and work plans.
Create and maintain technical design artifacts describing application functionality, data models, interfaces, and integrations.
Engage and negotiate with stakeholders, make business recommendations, and effectively present findings to multiple levels of stakeholders.
Champion continuous improvement and foster 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.
5+ years of experience developing and implementing data science techniques.
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.
Hands-on expertise with GenAI frameworks and LLM APIs, including experience developing AI bots and agents with reasoning and tool-use capabilities.
Strong 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 with technical and non-technical stakeholders.
Effective and concise oral and written storytelling and insights communication skills.
An ability to manage multiple projects, changing deadlines, and priorities.
An ability to mentor and guide junior data scientists and data engineers.
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 to help lead projects to commercial value.
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.
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