Data Scientist – Machine Learning & GenAI

Recrute Action•Toronto, ON

1d•CA$70 - CA$80•Hybrid

About The Position

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.

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.

Nice To Haves

Experience with MLOps, Azure, Databricks, or Agentic AI is considered an asset.

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.