Quantitative Analyst

Location: Montreal

We build rules‑based market signals and the quantitative research behind them. We hire people who do serious analytical, engineering, or distribution work.

Quantitative Analyst
Toronto (Remote)

Own the analytical layer of the company: study how the signal behaves across regimes, quantify where it works and where it breaks, and turn that evidence into work that sharpens both the product and the Academy. This role is less about frontier research for its own sake and more about disciplined market analysis that holds up under scrutiny.

What you’d do

Analyze signal quality across market regimes, time windows, and failure cases, with a clear view of what is robust and what is conditional

Measure hit rate, forward return shape, drawdown behavior, and false‑positive patterns across the full history

Build repeatable analytical workflows in Python or R that make new questions easy to test and old conclusions easy to revisit

Evaluate new indicators, filters, and market features that could improve the read without bloating the system

Turn quantitative findings into clear internal memos and Academy work that stay faithful to the evidence

Track current market context closely enough to flag setups, anomalies, and changes worth deeper analysis

What we’d look for

Degree in statistics, applied mathematics, finance, physics, computer science, or a similarly rigorous quantitative field

3+ years in quantitative analysis, market research, or similarly analytical work where the standard of proof actually mattered

Comfort with Python or R, market data, and reproducible analysis workflows that can survive review by others

Strong statistical instincts and the discipline to distinguish a clean story from a true one

Clear writing that does not smuggle in more certainty than the evidence allows

Intellectual honesty, especially when the answer is inconclusive, messy, or inconvenient to the story

Markets Analyst
Toronto (Remote)

Track the macro and market context that surrounds the signal: monitor regime conditions, document what the signal does and does not catch, and turn that ongoing analysis into Academy work that is honest about limits and useful to a disciplined investor.

What you’d do

Monitor macro conditions — VIX, Fed policy, labor, inflation — and maintain an ongoing read of where the market sits in the regime framework

Document signal performance in real time: what fired, what the context was, and what the outcome was as it matures

Write Academy articles and research notes that expand the educational layer without overstating what the signal can do

Identify macro or structural developments worth covering and propose content that would genuinely help a long‑term ETF investor reason through them

Review historical episodes to surface patterns, edge cases, and failure modes worth documenting publicly

Maintain intellectual honesty in all written output — conclusions stated at the confidence the evidence actually warrants

What we’d look for

Degree in economics, finance, statistics, or a similarly analytical field

2+ years tracking macro or equity markets in a research, analysis, or editorial capacity where accuracy mattered more than volume

Comfort reading and interpreting market data — price series, economic releases, Fed communications — without needing them translated

Clear, direct writing that does not dress up uncertainty as conviction

Genuine interest in how markets behave under stress, not just in normal conditions

The discipline to say nothing when there is nothing worth saying

Own the data and runtime layer that makes the research executable: ingestion, validation, persistence, internal APIs, and delivery infrastructure behind a product that has to be reliable every day it matters. In practice that means working across a Python…