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Data Scientist

Job in Toronto, Ontario, C6A, Canada
Listing for: Compunnel, Inc.
Full Time position
Listed on 2026-06-19
Job specializations:
  • IT/Tech
    Data Analyst, Data Scientist, AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 80000 - 100000 CAD Yearly CAD 80000.00 100000.00 YEAR
Job Description & How to Apply Below

The Data Scientist will support the development and optimization of a GenAI-powered sales enablement platform designed to help sales advisors interact with enterprise data through a conversational interface. This role focuses on preparing and analyzing complex datasets, generating insights, and supporting evaluation processes for LLM-driven features. The Data Scientist will collaborate with product, sales, and engineering teams to improve data pipelines, enhance analytics capabilities, and ensure reliable AI-driven insights that support sales workflows.

The position requires strong analytical and programming skills, hands-on experience with Python, SQL, and modern data platforms, and familiarity with LLM integrations and machine learning techniques.

KEY RESPONSIBILITIES
  • Prepare, clean, and analyze datasets used for training, validating, and evaluating GenAI and LLM-based features
  • Collaborate with product, sales, and business stakeholders to understand advisor workflows, data needs, and key performance metrics
  • Build dashboards and reporting solutions to track adoption, performance, and business impact of sales enablement tools
  • Support prompt evaluation, annotation, and quality assurance processes to improve AI-generated outputs
  • Contribute to the development of structured knowledge bases, taxonomies, and metadata to support RAG-based systems
  • Generate insights to improve sales processes and enhance advisor and end-user experiences
  • Develop analytics-driven solutions that support business goals and operational improvements
  • Analyze large and complex datasets and connect multiple internal data sources for unified insights
  • Translate analytical findings into clear business recommendations for stakeholders
  • Document data sources, methodologies, and processes to support continuous improvement
  • Collaborate with subject matter experts to understand business processes and develop analytical approaches
  • Assist with ongoing improvement of data pipelines and LLM integration with conversational interfaces
  • Support optimization of data and LLM pipelines that power the internal Data Copilot platform
  • Log tasks and project updates using collaboration tools such as Jira
  • Provide guidance and feedback to junior analysts or data scientists when needed
REQUIRED QUALIFICATIONS
  • 3–5 years of experience as a Data Scientist, Data Analyst, or in a related analytical role
  • Strong proficiency in Python for data analysis and machine learning workflows
  • Strong SQL skills and experience working with relational databases
  • Hands‑on experience with Git version control including branching and pull requests
  • Experience with exploratory data analysis, feature engineering, and model evaluation techniques
  • Proficiency with BI tools such as Power BI, Tableau, or similar visualization platforms
  • Experience working with complex datasets across multiple systems
  • Familiarity with LLM implementations, prompt engineering, and LLM guardrails
  • Strong problem‑solving skills and the ability to translate complex technical findings into business insights
  • Ability to work independently and manage loosely defined tasks in a fast‑paced environment
  • Strong communication and collaboration skills for working with cross‑functional teams
  • Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, or equivalent technical experience
PREFERRED QUALIFICATIONS
  • Experience working with Databricks, Spark, or modern data engineering platforms
  • Exposure to RAG pipelines or GenAI data architectures
  • Experience with Azure cloud environments
  • Familiarity with MLOps practices
  • Background working with sales datasets or sales operations workflows
  • Exposure to insurance industry data models or advisor‑based business environments
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