Data Scientist

Hi, we're HUB!

We are a leading North American insurance brokerage that advises businesses and individuals on how to reach their goals. When you partner with us, you're at the center of a vast network of risk, insurance, employee benefits, retirement and wealth management specialists that bring clarity to a changing world with tailored solutions and unrelenting advocacy – so you're ready for tomorrow.

The Opportunity!

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.

What You'll Bring to Our Team -

• 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.

Skills & Competencies:

Must-Haves

• 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.

A Day in the Life -

Analytics and Modelling:

• 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 q