Quant Research Analyst – Pattern Discovery & Signal Correlation (AI-Driven Trading)
# Description:
We are a stealth FinTech startup using proprietary data mining and machine-learning models to predict short-term market volatility and price movement. Our system produces probability scores and trade picks, and we are actively testing automated trading strategies.
We’re looking for a quant-oriented finance analyst to analyze our model outputs and identify additional technical, statistical, or market patterns that correlate—or anti-correlate—with gains and losses leading into purchase points.
This role is focused on signal discovery, validation, and interaction analysis, not discretionary trading. You must be quant-focused, be fluent in english and be able to read/write/speak coherently.
# What you’ll do:
- analyze our probability scores and historical picks vs. realized outcomes
- identify technical indicators or derived signals that:
- strengthen winning setups
- warn of false positives
- negatively correlate with returns
- test combinations and interactions between our ML scores and classic quant indicators
- evaluate indicators commonly used on TradingView (or equivalents), including:
• momentum (RSI, stochastic, MACD variants)
• trend (SMA/EMA structures, regime filters)
• volatility (bollinger bands, ATR, squeezes)
• volume & flow (OBV, volume profile, custom flow metrics)
- assess whether community or advanced indicators (e.g. squeeze momentum, wave-based or regime indicators) add predictive value
- produce clear, evidence-based conclusions (what helps, what hurts, what’s redundant)
# What we’re looking for:
- strong quantitative background in trading, market microstructure, or signal research
- experience analyzing technical indicators beyond surface-level usage
- comfort with python (pandas, numpy, scipy) and statistical validation
- ability to reason in probabilities, distributions, and conditional outcomes
- disciplined approach to avoiding overfitting and false correlations
# Nice to have:
- experience with short-term or swing-based strategies
- familiarity with TradingView / Quant indicators (conceptually or hands-on)
- background in systematic trading, factor research, or alpha discovery
- experience working alongside ML-driven prediction systems
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