We are a systematic quantitative trading firm building fully automated strategies across crypto markets (CEX & on-chain). We run a research-driven pipeline: idea → data → features → backtest → live → iterate.
This is not discretionary trading. This is engineering alpha.
You’ll be working directly on strategy R&D and data infrastructure: collecting, cleaning, structuring, and turning raw market data into profitable trading systems.
* What You Will Do
* Strategy Research & Development (Core)
Research, design, and test systematic trading strategies across:
Crypto spot, perps, futures
Intraday, swing, and statistical arbitrage horizons
Turn hypotheses into measurable signals
Build:
Entry/exit logic
Position sizing logic
Risk filters & regime filters
Perform:
Robust backtests
Out-of-sample testing
Parameter stability & overfitting checks
Analyze PnL drivers, failure modes, and edge decay
* Data Collection & Data Engineering (Equally Important)
Build and maintain data pipelines for:
OHLCV (multi-timeframe)
Trades, orderbook, funding, open interest
Liquidations, on-chain metrics, flows
Work with:
Exchange APIs (Binance, Bybit, OKX, etc.)
Flat files / time-series databases
Responsibilities:
Data cleaning
Timestamp alignment
Corporate-action-like adjustments (forks, symbol changes, contract rolls)
Outlier detection
Missing data handling
Build research-grade datasets, not garbage-in-garbage-out.
* Feature Engineering & Factor Research
Design alpha factors from:
Price action
Volume, order flow, liquidity
Volatility, funding, basis, positioning
Cross-asset and cross-timeframe relationships
Build:
Single-bar features
Multi-bar structural features
Regime and context variables
Analyze:
Feature stability
Feature interactions
Feature decay
* Research Infrastructure & Tooling
Improve:
Backtesting framework
Research workflow
Experiment tracking
Build tools for:
Fast iteration
Batch experiments
Strategy comparison
Portfolio-level simulations
* What We’re Looking For
Must-Have
Strong quantitative / analytical thinking
Strong Python (pandas, numpy, vectorization, not for-loops everywhere)
Comfortable with:
Time-series data
Backtesting
Statistical analysis
Experience handling messy real-world data
Able to think in:
Signals
Distributions
Expected value
Regimes
Understands overfitting is the enemy
Big Plus
Experience in:
Crypto market microstructure
Order book data
On-chain data
HFT / intraday systems
C++ experience
SQL / ClickHouse / time-series DBs
Experience building research pipelines, not just notebooks
Has actually run strategies or done serious backtests before
* Background
Degree in:
Math / CS / Engineering / Physics / Stats / Econ or similar
But honestly:
If you’re good, we don’t care about paper credentials.
* What You Get
Work on real capital, real strategies, real impact
Direct influence on PnL
Access to:
Full research stack
Large datasets
Serious compute
Competitive compensation + performance upside
No politics. No fluff. Just build edge.
* Who Should NOT Apply
If you:
Want to do “crypto research” by reading Twitter
Think indicators = strategies
Can’t tell the difference between backtest and reality
Want hand-holding and step-by-step instructions
This is not that job.
* Interview Process
Discussion of:
Past research or projects
How you think about alpha
Practical task:
Data cleaning / feature extraction / small research problem
We care about how you think, not buzzwords.
Kindly note that only shortlisted candidates will be notified.
Related Job Searches:
- Company:
Varsity Holdings - Designation:
Crypto Quant Researcher (Strategy Development & Data) - Profession:
Banking / Finance - Industry:
Finance - Location:
Downtown Core
