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Data Scientist I – GenAI & Machine Learning

  • Toronto, ON, M5J 0B6, Canada

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Job details

Job type
Contract
Experience
Open to recent graduates and candidates with 0-3 years of relevant experience.
Working hours
37.50 hours per week
Posting language
English
Posted

Original 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.

Why Recruit Action?

Recruit Action (agency permit: AP-2504511) provides recruitment services through quality support and a personalized approach. As part of the screening process, some applications may be reviewed using artificial intelligence tools. Only candidates who meet the hiring criteria will be contacted.


More about this job

Responsibilities

Support the development and deployment of machine learning and generative AI solutions, including AI agents, chatbots, retrieval systems, and predictive models. Contribute to feature engineering, model training and evaluation, technical design documentation, and collaborative analytics projects.

Requirements

A bachelor's degree in computer science, statistics, mathematics, or a related field, or equivalent experience, is required; the role is open to recent graduates and candidates with 0–3 years of relevant experience. Candidates need Python, complex SQL and PySpark, machine learning, data transformation, API and microservices knowledge, and experience with GenAI, LLMs, RAG, chatbots, and AI agents; Reliability Clearance is required.

Skills

  • Python
  • SQL
  • PySpark
  • Machine Learning
  • Generative AI
  • Large Language Models
  • Prompt Engineering
  • Retrieval-Augmented Generation
  • AI Agents
  • LangChain
  • LangGraph
  • CrewAI
  • Vector Databases
  • Embedding Models
  • AWS
  • Data Transformation

Visa sponsorship

Not detected in the job text

Categories

  • Data & Analytics
  • Technology
  • Software
  • Science & Research

Keywords

  • Data Science
  • Machine Learning
  • Generative AI
  • Python
  • SQL
  • PySpark
  • Supervised Learning
  • Unsupervised Learning
  • Feature Engineering
  • Model Training
  • Model Evaluation
  • Prompt Engineering
  • Large Language Models
  • LLM APIs
  • Retrieval-Augmented Generation
  • AI Agents
  • Chatbots
  • Agentic Frameworks
  • LangChain
  • LangGraph
  • CrewAI
  • Vector Databases
  • Embedding Models
  • APIs
  • Microservices
  • Cloud-Native Development
  • AWS SageMaker
  • AWS Lambda
  • AWS Bedrock
  • AWS Glue

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