GenAI Data Analyst (Python / ML / LLM)
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.
What is in it for you:
- Salaried: $45-53 per hour.
- Incorporated Business Rate: $54-62 per hour
- Full-time position: 37.50 hours per week.
- Hybrid: 3 days/week in Toronto office.
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.
What you will need to succeed:
- 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.
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.
# MFCJP00016669
Pay: $45.00-$62.00 per hour
Benefits:
Application question(s):
- This role is hybrid in Toronto — are you comfortable working 3 days per week in-office?
Experience:
- Python (pandas, data processing, scripting): 3 years (required)
- ML basics (EDA, feature engineering, testing): 2 years (required)
- LLM / GenAI (prompting, context, evaluation): 2 years (required)
- Data analysis (complex, multi-source datasets): 3 years (required)
- BI tools (Power BI, Tableau, dashboards and reporting): 2 years (required)
- Git / GitHub (version control, collaboration): 2 years (required)
- Azure / Databricks (cloud data and analytics environment): 1 year (preferred)
- RAG pipelines (retrieval, vector databases, knowledge bases): 1 year (preferred)
- MLOps (model deployment, monitoring, pipelines): 1 year (preferred)
- Sales / CRM data (sales operations, advisor workflows): 1 year (preferred)
- Insurance or financial services domain: 1 year (preferred)
Work Location: Hybrid remote in Toronto, ON M4W 1E6