Staff Data Scientist

Customer behavior is a signal. Most companies collect it. Few know how to read it.

You will be one of the early data science hires shaping how Purpose understands the clients on our wealth platform — and how the platform responds to what we learn. You'll build the measurement architecture, behavioral models, and causal infrastructure that connects product decisions to client outcomes. Not what looks like it works. What actually does.

This is a foundational role at a foundational moment. The intelligence layer you build will shape how Purpose designs, measures, and evolves its platform for years — and increasingly, how our AI systems understand and serve the clients who depend on us.

You're an engineer as much as a scientist — you own the work from raw event data to deployed system, and you hold yourself accountable for whether it changed how the business operates.

Recurring insight generation — automated analytics workflows run on schedule with defined guardrails. You design the system and own the governance; you don't write the weekly summary.

Boilerplate feature engineering — AI coding assistants accelerate pipeline construction from event data. You define what to build and review what the tools produce.

First-pass segmentation refreshes — classification and scoring jobs re-run automatically. You set the standards and monitor for drift, you don't rerun the job.

Standard visualization and reporting — AI tools increasingly generate monitoring views from structured data. You define what signals matter; the generation is handled.

Determine what is worth measuring — in a platform generating thousands of behavioral signals, the most valuable judgment is deciding which three metrics actually reflect client value and which are vanity.

Push back on attribution that flatters — AI systems generate correlations at scale. You are the person who asks whether the relationship is causal, whether the experiment is valid, and whether the conclusion will hold when the business acts on it.

Translate behavioral reality into a measurable operating model — starting from how clients actually make financial decisions, not from how the product team assumes they do.

Design AI analytics systems with the rigor of a statistician — evaluating LLM-generated insights with the same calibration and drift standards you apply to your own models.

Build the institutional trust that makes product and business leaders act on quantitative findings, including findings they didn't want.

The client intelligence architecture: the measurement model that maps how clients discover, onboard, activate, and engage with Purpose's wealth platform — with FOMs that connect platform activity to real client outcomes.

Behavioral models that change decisions: client segmentation, lifecycle classification, churn prediction, propensity scoring, and behavioral clustering — validated against real outcomes and governed for drift.

Causal measurement with discipline: experiment design and analysis, causal inference when experiments aren't possible, and a consistent standard for what counts as evidence that a product or AI intervention actually worked.

Agentic analytics governance: you set the standards for what AI-generated insight requires human review, what guardrails govern automated outputs, and how the intelligence layer stays accurate and auditable in a regulated environment.

Deep fluency in Python and SQL — owning work end-to-end from raw event data to deployed software, with production-grade testing and documentation standards.

Proven experience building and evaluating predictive models in production-like settings: classification, survival models, segmentation, scoring, calibration, and cost-sensitive decision frameworks.

Applied causal inference — A/B testing, matching/propensity, diff-in-diff, regression discontinuity. You know the difference between a valid experiment and a flattering one.

Experience in a regulated industry (financial services, healthcare, or equivalent) — you treat data access controls, model auditability, and responsible use of customer data as core professional standards.

You've been early on a data science or analytics team and built measurement and modeling foundations before the playbook existed — you know what it means to make something from scratch that others depend on.

You have experience evaluating LLMs or AI-generated outputs with statistical rigor: calibration, drift detection, bias assessment. You apply the same skepticism to AI outputs that you apply to your own models.

You've used AI-assisted development tools (GitHub Copilot, Claude Code, Cursor, or equivalent) as a genuine productivity multiplier in data science workflows.

You have applied behavioral economics or behavioral finance principles to how you designed metrics or interpreted client behavior — you think about why clients make financial decisions, not just what they do.

A measurement architecture is live — FOMs that connect platform activity to client outcomes are defined, instrumented, and being used by product and business leaders to make decisions they couldn't make before.

At least two behavioral models are in production — churn prediction, propensity scoring, or lifecycle classification — with monitoring, documentation, and governance that any engineer on the team can maintain.

Causal measurement is the standard — at least one major product or AI intervention has been evaluated with a valid causal method, and the findings have changed how the team approaches attribution.

The AI analytics layer has governance — the standards for what AI-generated insight requires human review, and what guardrails govern automated output, are defined, documented, and in use.

You've become the person that product and business leaders bring their hardest measurement questions to — because they trust that what you tell them reflects reality, including when reality is inconvenient.

Innovation – You bring new ideas, challenge the status quo, and find better ways of building. You don’t wait for permission to experiment.

Courage – You speak up when something isn’t working, take difficult stands on technical quality, and act with conviction even when it’s uncomfortable.

Owner’s Mindset – You take full accountability for your squad’s outcomes. You treat the platform, the team, and the business as your own.

Client Focus – You place client outcomes at the center of every architectural and delivery decision.

Winning Through Individual and Collective Growth – You invest in your own development and actively contribute to the growth of every engineer around you.

We are one of Canada's Top Small & Medium Employers' 2023, 2024, 2025 & 2026.

We believe in innovation and a vibrant culture - work for an innovative, people-first, financial services firm that values entrepreneurialism.

We believe in a flexible work structure – A flexible hybrid work model that empowers you to do your best work whether at home or the office.

We care about your rewards - Competitive compensation including equity program.

We care about your health – comprehensive group health and dental benefits and life insurance at little to no cost to you. We also offer a Lifestyle Spending Account for all your wellness needs.

We care about your quality of life - flexible paid time-off policy with unlimited vacation days, and flexible sick and mental health days.

We care about your family - Paid parental leave for eligible employees with a top-up.

We care about your future – Generous Group RRSP matching and an optional TFSA program.

We care about your development – We offer training opportunities and tuition support year-round.

Purpose Unlimited is an equal employer and we are dedicated to fostering an inclusive and barrier-free work environment for all employees and candidates. We encourage all qualified candidates to apply and if accommodation is required during any stage of the recruitment process, please contact any member of the People & Culture team at PeopleCultureTeam@purpose-unlimited.com. We thank all applicants for their interest; however, only those selected for interviews will be contacted.

Our work philosophy is a hybrid model allowing for flexibility and collaboration.

Applicants must be legally entitled to work in Canada. Immigration sponsorship is not offered for this role.

We may use artificial intelligence technology to assist in screening, assessing, or selecting applicants for this position. Final hiring decisions are made by qualified human reviewers.