Data Scientist

HUB International•Abbotsford, BC

2d•CA$90,000 - CA$115,000•Onsite

About The Position

This is a permanent, full-time position, reporting to the Director, Business Intelligence at HUB Canada West (HCW). As the Data Scientist, you are the technical engine of a small, high-leverage analytics function. This role exists to turn HCW's growing data foundation across cloud data platforms and SQL into measurable business value: predictive models, self-serve analytics, intelligent automation, and AI-powered tools that change how branches and leadership make decisions. The successful candidate brings classical data science fundamentals (statistics, machine learning, experimental design) and is equally comfortable in the emerging world of AI engineering: prompting and orchestrating large language models, building reusable AI workflows, and integrating LLMs into BI products. As the discipline of data science continues to converge with AI engineering, we want someone who can move fluidly across both. You will operate as a senior individual contributor and a generalist problem solver. You enjoy ambiguity, you pick up new tools quickly, and you care about outcomes more than orthodoxy.

Requirements

5+ years of hands-on experience in data science, analytics engineering, advanced analytics, or a directly related applied technical role.
Bachelor's or Master's degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Data Science, Engineering, or similar). Equivalent demonstrated experience will be considered in lieu of formal credentials.
Background in insurance, financial services, or another regulated industry is a strong asset, with practical familiarity with concepts such as policies in force, premium, retention, and producer compensation.
A generalist, problem-solving mindset: comfortable picking up new tools, languages, and platforms; energized by ambiguity rather than blocked by it.
Strong SQL skills, including comfort with complex joins, and query optimization against large datasets.
Strong Python skills for analysis, modelling, and automation (pandas, scikit-learn, NumPy), plus practical experience with ML Algorithms such as Random Forest, Recommender Systems or Clustering.
Demonstrated production experience with Power BI, including data modelling, DAX, and the ability to design dashboards that leadership can intuitively use.
Working knowledge of statistics, machine learning fundamentals, and model evaluation; able to choose the right tool for the question rather than reaching for the most complex one.
Demonstrated ability to use Claude (or other AI tools like ChatGPT, Copilot, or equivalent) as a serious working tool: structured prompting, iterative refinement, awareness of failure modes, guardrails, and the discipline to verify outputs.
Excellent written and verbal communication, with the ability to translate technical findings into business decisions and to write clearly for executive audiences.

Nice To Haves

Experience building Claude Skills, artifacts, MCP server integrations, or agentic AI workflows.
General familiarity with Google Cloud Platform, particularly BigQuery, is a plus as HCW's data environment evolves.
Experience with light data engineering tooling such as dbt, Airflow, or Cloud Composer.
Comfort with version control (Git), code review practices, and collaborative development workflows.
Familiarity with Canadian data privacy requirements (PIPEDA) and the practical realities of working with sensitive customer and HR data.

Responsibilities

Design and deliver applied analytics and machine learning solutions across sales, retention, finance, operations, and talent (for example, customer segmentation, churn and renewal probability, book of business analytics, producer performance modelling).
Translate ambiguous business questions from leadership into structured analytical problems, choose the right level of methodological rigour for the decision at hand, and communicate results in plain language.
Apply solid statistical thinking to model evaluation, uncertainty quantification, and the honest framing of data quality limitations.
Build and maintain production-grade prompts, Claude Skills, and AI-assisted workflows that extend HCW's analytical reach (for example, manager-facing prompt playbooks for self-serve call analytics).
Integrate large language models with HCW data sources to enable natural-language analytics, automated narrative generation, and intelligent document processing.
Stay current with the rapidly evolving AI tooling landscape and bring promising techniques into HCW thoughtfully, with appropriate guardrails.
Extract, join, and transform data from HCW's data warehouse, Power BI semantic layer, and other source systems to support analytical work. Note: this is not a heavy data engineering role. Platform, ingestion, and core pipeline work sits primarily with the Canada Data team and Corporate IT. The Data Scientist is expected to access and shape data, not to build the warehouse.
Engineer features, build reproducible analytical pipelines, and document data lineage and assumptions.
Build interactive Power BI dashboards that make analytical output usable by branch managers, regional leadership, and corporate executives.
Develop reusable Power BI templates and data structures that let branch managers, regional leadership, and corporate executives self-serve their own analysis without relying on ad hoc analyst support.
Track adoption of Power BI reports and dashboards, and surface how they are being used to drive measurable business value across branches and leadership.
Identify high-friction manual reporting and operational workflows, then automate them using Python, scheduled data pipeline jobs, Power Automate, or AI-powered workflows.
Design automation that is observable, recoverable, and trusted by the business.
Work directly with the COO, regional leadership, and operational managers to scope work and present findings.
Coach and uplift the broader BI community of practice on Python, SQL, AI tooling, and analytical thinking.
Collaborate with the Canada Data team and Corporate IT on data standards, governance, and shared platform decisions.