Data Scientist – GenAI, LLM & Sales Analytics (Insurance)
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- Toronto, ON
- Permanent
- Full-time
- Salaried: $40-50-per hour.
- Incorporated Business Rate: $50-60 per hour.
- 9-month contract.
- Full-time position: 37.50 hours per week.
- Remote on Monday and Friday.
- On-site Tuesday to Thursday.
- Analyze, clean, and prepare complex datasets to support the development and evaluation of AI-driven features.
- Collaborate with business stakeholders to understand sales workflows, define requirements, and align on key performance indicators.
- Develop dashboards and reporting tools to monitor adoption, performance, and business impact.
- Support prompt evaluation, annotation, and quality assurance processes for AI-generated outputs.
- Build and maintain structured knowledge bases, taxonomies, and metadata for retrieval-augmented systems.
- Generate insights to improve sales processes and enhance advisor and client experiences.
- Deliver analytics projects of moderate complexity aligned with business goals and timelines.
- Integrate and analyze data across multiple internal systems to uncover trends and opportunities.
- Translate analytical findings into clear business recommendations for stakeholders.
- Document processes, data sources, and methodologies to support continuous improvement.
- Collaborate with cross-functional teams and contribute to knowledge sharing and best practices.
- Provide guidance and informal mentorship to junior team members when required.
- Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, or equivalent technical experience.
- 3 to 5 years of experience in data analysis or data science, ideally within sales operations or the insurance industry.
- Strong proficiency in Python for data analysis, modeling, and automation.
- Solid understanding of statistical methods and machine learning techniques.
- Experience with BI tools such as Power BI, Tableau, or similar platforms.
- Familiarity with relational databases, data modeling, and large-scale data environments.
- Knowledge of GenAI concepts, including prompt engineering, LLM evaluation, and guardrails.
- Experience with Git and version control best practices.
- Ability to work with ambiguous problems and translate them into structured analytical approaches.
- Strong communication skills to convey technical insights to non-technical stakeholders.
- Exposure to tools and environments such as Azure, Databricks, MLOps, or RAG pipelines is an asset.
- Strong problem-solving mindset with the ability to manage priorities and navigate complex datasets in fast-paced environments.
- Ability to work collaboratively while maintaining autonomy and ownership of deliverables.