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About the Role
Work on high-visibility AI and data initiatives within the insurance sector, combining machine learning, GenAI, predictive analytics, and modern data tools to support strategic business decisions. This hybrid opportunity offers a collaborative and fast-paced environment where innovation, problem-solving, and impactful analytics are at the center of every project.
Responsibilities
- Prepare, clean, and analyze datasets for ML and AI features from complex and fragmented internal data sources.
- Leverage LLMs to create features from unstructured data.
- Design and build segmentation and predictive models for customer and advisor analytics.
- Own the feature engineering pipeline for ML and AI models.
- Collaborate with business stakeholders to understand workflows, data requirements, and key performance metrics.
- Build dashboards and reporting assets to deliver insights to business stakeholders.
- Contribute to the development and evaluation of modular GenAI features, including RAG systems, NL-to-SQL solutions, and agentic workflows.
- Develop and implement analytics-enabled solutions supporting business goals and process improvement initiatives.
- Translate analytical findings into business language and recommend solutions to stakeholders and leadership teams.
- Document data sources, contribute to structured processes, and support continuous improvement tracking activities.
- Participate in daily project updates with the core team.
- Communicate with business partners to confirm requirements and clarify timeline constraints.
- Propose and implement technical solutions aligned with business needs and project deadlines.
- Perform hands-on data preparation, analysis, and development activities.
- Draft presentation materials outlining proposed solutions for business stakeholders.
- Accurately track and manage tasks within Jira.
Requirements
- Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, or equivalent technical experience.
- 3-5 years of experience as a Data Analyst, Data Scientist, or in a related analytical role within insurance, sales support, finance, or similar environments.
- Strong Python programming skills with experience using libraries such as pandas, NumPy, scikit-learn, PySpark, or similar tools.
- Strong SQL experience and proficiency with data modeling concepts.
- Experience with BI tools such as Power BI, Tableau, or similar platforms.
- Demonstrated experience engineering complex features from large, multi-source datasets and assessing feature quality.
- Experience with end-to-end model development, including problem framing, data preparation, feature engineering, model training, validation, and deployment support.
- Experience with statistical methods and machine learning techniques such as regression, clustering, PCA, decision trees, and survival analysis.
- Strong understanding of ML fundamentals, including exploratory data analysis, feature engineering, and model testing.
- Experience with GitHub and Git version control tools.
- Knowledge of LLM concepts, including context engineering, prompt engineering, and LLM guardrails.
- Ability to translate ambiguous business questions into structured analytical approaches.
- Ability to communicate technical concepts clearly to business stakeholders and translate complex technical components into understandable business requirements.
- Strong problem-solving mindset with the ability to make confident technical decisions.
- Ability to work autonomously, demonstrate ownership, and appropriately escalate issues when required.
- Curiosity about GenAI technologies and eagerness to learn LLM workflows, evaluation techniques, and best practices.
- Experience with MLOps, Azure, Databricks, or Agentic AI is considered an asset.
Skills
- Machine Learning
- GenAI
- Predictive Analytics
- Python
- pandas
- NumPy
- scikit-learn
- PySpark
- SQL
- Data Modeling
- Power BI
- Tableau
- Feature Engineering
- Model Development
- Statistical Methods
- Regression
- Clustering
- PCA
- Decision Trees
- Survival Analysis
- ML Fundamentals
- Exploratory Data Analysis
- Model Testing
- GitHub
- Git
- LLM Concepts
- Context Engineering
- Prompt Engineering
- LLM Guardrails
- MLOps
- Azure
- Databricks
- Agentic AI
Location
- Toronto, Ontario
Work Type
- Hybrid
- Full-time
- Contract
Experience Level
- 3-5 years
Education Level
- Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, or equivalent technical experience
Salary/Compensations
- $60-70 per hour
- $70-80 per hour (Incorporated Business Rate)
Benefits
- 6-month contract with the potential for permanent employment
- Day schedule
- 37.50 hours per week
About the Company
- 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.