Job Description

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