Junior Data Scientist to support advanced analytics and GenAI initiatives using Python, SQL, and machine learning - 1837

S.I. Systems Ltd.

Toronto

Hybrid

CAD 70,000 - 100,000

Full time

2 days ago
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Job summary

S.I. Systems Ltd. in Downtown Toronto seeks a Junior Data Scientist to support advanced analytics and GenAI initiatives using Python, SQL, and ML. You’ll work on data analysis, model development, and GenAI applications in a large insurance environment.

The role emphasizes execution, cross-functional collaboration, mentoring junior colleagues, and delivering on multiple projects during a 12-month maternity leave coverage with a hybrid on-site schedule.

Qualifications

  • 0-3 years experience in Data Science/ML initiatives
  • Bachelor’s degree in CS, Stats, Math, or related field, or equivalent experience
  • Proficient in Python for data science and GenAI development
  • Experience with SQL and PySpark for data extraction and integration
  • ML experience with supervised and unsupervised models
  • Experience transforming and manipulating structured and unstructured data
  • Understanding of APIs, microservices, cloud-native development
  • Experience with GenAI frameworks and LLM APIs; AI bots/agents with reasoning and tool-use
  • Familiarity with RAG techniques and embedding models
  • Experience with chatbot development and conversational AI design
  • Strong communication to translate technical concepts to stakeholders
  • Ability to manage multiple projects with changing deadlines
  • Experience mentoring junior data scientists and engineers
  • Strong problem-solving and analytical skills

Responsibilities

  • Translate business goals into analytical problems and select appropriate algorithms/GenAI architectures
  • Collaborate in cross-functional teams to develop ML/data science products including GenAI applications
  • Apply descriptive, predictive, and ML techniques from design to implementation
  • Focus on feature engineering, model training/evaluation, and prompt engineering for LLMs

Job description

Junior Data Scientist to support advanced analytics and GenAI initiatives using Python, SQL, and machine learning - 1837

Location: Downtown Toronto - Hybrid (3 days on-site with Thursday being the team collaboration day)

Must be eligible for Reliability Clearance (Must have lived in Canada for 5 consecutive years)

Join a data analytics team supporting advanced analytics and GenAI initiatives within a large insurance environment. This role focuses on execution-oriented support for data science and machine learning projects, including structured and unstructured data analysis, model development, and GenAI applications. The position supports senior team members on focused project delivery within cross-functional teams. This is a 12-month contract supporting coverage for a maternity leave.

Must Haves
  • 0-3 years supporting Data Science and Machine Learning initiatives
  • Bachelor’s degree in computer science, Statistics, Mathematics, or related field, or equivalent experience
  • 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
  • Demonstrated experience in data transformation, data manipulation, 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 constantly 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
Nice to Have
  • Experience with AWS services including SageMaker, Lambda, Bedrock, and other AI/ML 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.
Responsibilities
Data Science and Machine Learning
  • Translate business goals into analytical problems; 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 (LangChain, LangGraph, CrewAI etc.)
  • 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 with effective presentations of findings at multiple levels of stakeholders
  • Champion continuous improvement and foster innovation within the analytics comuni

Duration: 12 months to start

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