PhD Quant Researcher

Why This Role Exists

NK Securities is building a research-driven trading platform where models, not opinions, drive decisions. We are hiring PhD-level researchers to work on pricing models, market microstructure, and machine-learning-driven alpha that directly impact live trading systems.

This is not a sandbox role. Your work will move capital, trade markets, and be evaluated in production.

If you enjoy:

• Turning theory into models that survive noisy, non-stationary data
• Seeing your research deployed and stress-tested in real markets
• Working end-to-endfrom idea to impact

,this role is designed for you.

What Youll Work On

You will operate as a research owner, not a support function.

Research & Modeling

• Design pricing and fair-value models at short horizons
• Model order-book dynamics, liquidity, impact, and micro-price behavior
• Research alpha signals using statistical learning and ML/AI methods
• Develop models robust to regime shifts, feedback loops, and adversarial noise

• Apply machine learning and modern AI techniques to high-frequency market data
• Explore deep learning, representation learning, and sequence models where justified
• Balance interpretability, robustness, and predictive power
• Build models that generalizenot just optimize backtests

• Run large-scale experiments and rigorous backtesting
• Define validation criteria, failure modes, and monitoring metrics
• Partner with engineers and traders to deploy models into live systems
• Continuously iterate based on real performance feedback

You dont need to know everythingbut you should be excited to learn and extend:

Pricing & Microstructure

• Fair-value and micro-price models
• Order-flow and liquidity models
• Spread, impact, and short-horizon price dynamics

• Time-series and state-space models
• Volatility and correlation structures
• Signal decay and stability modeling

• Feature-based ML for alpha discovery
• Representation learning for structured market data
• Deep learning models used selectively and critically

Education

• PhD (completed or near completion) in Mathematics, Statistics, Computer Science, Physics, Electrical Engineering, Operations Research, or related fields
• Strong research pedigree and demonstrated ability to solve open-ended problems

• Deep understanding of probability, statistics, and linear algebra
• Ability to translate abstract ideas into testable, empirical models
• Comfort reasoning under uncertainty and imperfect data
• Evidence of original thinking (papers, thesis work, significant projects)

• Strong Python for research and experimentation
• Experience with ML / AI frameworks (e.g., PyTorch, TensorFlow)
• Comfort working with large datasets and computational experiments
• Exposure to C++ or performance-oriented systems is a plus, not a requirement