PhD Quant Researcher

Gurugram

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-end—from idea to impact

,this role is designed for you.

What You’ll 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

Machine Learning & AI

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 generalize—not just optimize backtests

From Research to Production

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

Model Classes You’ll Encounter

You don’t need to know everything—but 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

Statistical Models

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

ML / AI Models

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

Who We’re Looking For

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

Research Strength

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)

Technical Skills

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