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 recent 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 research pipeline and a Cloudflare Worker stack that owns D1, webhooks, and customer‑facing runtime behavior.

What you’d do

Maintain and extend the end-to-end signal pipeline, from Python‑side computation through Worker‑side ingestion, persistence, and delivery

Own TypeScript and SQL work inside a Cloudflare Worker architecture that handles Stripe, authentication, internal endpoints, and D1 reads/writes

Design and evolve schema, migrations, and internal API contracts so new product capabilities remain explicit, auditable, and easy to reason about

Implement and validate new features, filters, and model components on market time‑series without compromising reproducibility

Write documentation, review code, and promote architectural clarity so responsibilities stay clean across the Python and Worker boundaries

Keep the daily system observable, unit‑tested, and resilient under failure, including the messy edge cases that only appear in production

What we’d look for

Degree in computer science, engineering, mathematics, or a related technical field

3+ years building production systems in a data‑heavy environment, ideally where reliability and auditability were non‑negotiable

Strong Python plus enough TypeScript fluency to work comfortably in a serverless backend that owns real production state

Good engineering judgment around SQL schema design, API contracts, testing, code review, and change management

Experience with scheduled systems, external data sources, production reliability, and cloud infrastructure; Cloudflare familiarity is a plus

The ability to collaborate across research and engineering work without treating either side as someone else’s problem

A bias for simple, inspectable systems with clear ownership boundaries over clever but fragile machinery

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📌 Quantitative Analyst (Quebec City)
🏢 DoubleTrends
📍 Quebec City