LLM)

Recrute Action•Toronto, ON

4d•CA$45 - CA$62•Hybrid

About The Position

High-impact role in the insurance sector focused on advanced data analysis and GenAI-driven sales enablement. Leverage Python, BI tools, and machine learning concepts to transform complex datasets into actionable insights, build performance dashboards, and support AI-powered solutions in a collaborative hybrid environment.

Requirements

Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, or equivalent technical experience.
3 to 5 years of experience in a data analyst, data scientist, or related analytical role.
Experience in sales support or sales operations environments, with exposure to insurance industry workflows considered an asset.
Strong Python programming skills.
Proficiency with BI tools such as Power BI, Tableau, or similar platforms.
Knowledge of statistical methods and familiarity with machine learning techniques.
Experience working with large and complex datasets using structured analytical approaches.
Understanding of data modeling concepts, relational databases, and basic AI/ML toolkits.
Familiarity with GitHub and version control practices.
Knowledge of ML fundamentals including exploratory data analysis, feature engineering, and model testing.
Exposure to LLM concepts such as prompt engineering, context engineering, and guardrails.
Ability to translate complex technical concepts into clear business insights.
Strong problem-solving skills and ability to work autonomously.
Effective communication skills and ability to collaborate within a team environment.
Curiosity and willingness to learn GenAI workflows and evaluation techniques.

Nice To Haves

Exposure to insurance industry workflows

Responsibilities

Prepare, clean, and analyze datasets used for training, validating, and evaluating LLM-based or GenAI features.
Collaborate with product, sales, and business stakeholders to understand workflows, data requirements, and performance metrics.
Build dashboards and reporting assets to track adoption, performance, and business impact of sales enablement tools.
Support prompt evaluation, annotation, and quality assurance to ensure accuracy and reliability of AI-generated outputs.
Contribute to structured knowledge bases, taxonomies, and metadata supporting RAG-based systems.
Generate insights to optimize sales processes and improve advisor and end-user experiences.
Develop and implement analytics solutions aligned with business goals and deliver projects of moderate complexity.
Analyze complex datasets and connect multiple internal data sources.
Translate analytical findings into business recommendations for stakeholders and senior data scientists.
Document data sources, support structured processes, and enable continuous improvement tracking.
Engage subject matter experts and contribute to collaboration and knowledge sharing.
Provide guidance and feedback to junior analysts or data scientists when required.
Participate in daily project updates and communicate with business partners on requirements and timelines.
Propose and implement technical solutions aligned with business needs and deadlines.
Perform hands-on data preparation, analysis, and development tasks.
Prepare presentation materials to communicate proposed solutions.
Track tasks and progress using Jira.