Website:
grull.space
Job details:
About the Role
We are a start-up proprietary trading firm building a high-performance quantitative research capability in Bangalore. We are looking for a rigorous, intellectually curious Quantitative Analyst to join our systematic trading research team.
In this role, you will develop, backtest, and refine quantitative trading models across digital asset and alternative markets. You will work with large-scale market datasets (on-chain, order book, derivatives), apply statistical and machine learning methods to identify alpha signals, and collaborate closely with discretionary traders and engineers to bring strategies from research to production.
This is not a support role — you will own the full lifecycle from hypothesis generation through model validation to live performance monitoring.
If you think in distributions, not predictions, and want to build systematic strategies in a market where inefficiencies are abundant but fleeting, this is the role.
Key Responsibilities
- Design, develop, and backtest systematic trading strategies using statistical, econometric, and machine learning techniques across digital asset spot, derivatives, and DeFi markets.
- Identify and extract alpha signals from diverse data sources: order book dynamics, on-chain metrics, funding rates, liquidation cascades, sentiment data, and macro indicators.
- Build and maintain robust backtesting frameworks with proper handling of transaction costs, slippage, market impact, and survivorship/look-ahead bias.
- Develop market regime classification models to dynamically adjust strategy parameters and risk exposure.
- Conduct research on market microstructure, cross-exchange arbitrage opportunities, basis trading, and volatility surface dynamics in Digital assets derivatives.
- Collaborate with engineers to productionise validated models — define signal generation pipelines, feature engineering specifications, and real-time inference requirements.
- Monitor live strategy performance, investigate P&L attribution and drawdown drivers, and iterate on models based on production feedback.
- Prepare research memos, strategy documentation, and performance reports for senior leadership and risk oversight.
- Stay current on academic and industry developments in quantitative finance, market microstructure, and applied ML for trading.
- Contribute to the firm’s quantitative research codebase, ensuring code quality, reproducibility, and documentation standards.
Requirements - Must Have
- 2–5 years of experience in quantitative research, systematic trading, or quantitative risk management at a prop trading firm, hedge fund, or asset manager.
- Strong foundation in probability, statistics, time series analysis, and stochastic processes; comfort with hypothesis testing, regression, and dimensionality reduction.
- Proficiency in Python for research (NumPy, Pandas, SciPy, statsmodels, scikit-learn); experience with at least one deep learning framework (PyTorch or TensorFlow) is expected.
- Demonstrated experience building backtesting systems or strategy simulation frameworks with realistic cost and execution modelling.
- Familiarity with financial derivatives pricing, Greeks, and risk factor models; understanding of digital assets-specific instruments (perpetual futures, funding rates, basis).
- Strong programming discipline — version control (Git), testing, modular code architecture, and documentation.
- Ability to communicate complex quantitative concepts clearly to non-technical stakeholders.
- Willingness to work flexible hours aligned to 24/7 digital assets market cycles when strategies are in deployment or under observation.
Requirements Good to Have
- Experience with Hidden Markov Models, regime-switching models, or state-space models for financial applications.
- Exposure to gradient-boosted tree methods (XGBoost, LightGBM, CatBoost) for feature importance and signal generation.
- Familiarity with on-chain data analytics (Nansen, Dune, Santiment, Glassnode) and DeFi protocol mechanics.
- Experience with alternative data sources — social sentiment, news NLP, satellite data, or blockchain-native metrics.
- Knowledge of optimal execution algorithms (TWAP, VWAP, implementation shortfall) and market impact modelling.
- Advanced degree (Master’s or PhD) in a quantitative discipline: Mathematics, Statistics, Physics, Computer Science, Financial Engineering, or Econometrics.
- Professional certifications: CFA, FRM, CQF, or equivalent.
International Relocation Path
This role offers a structured path to relocation to Singapore or Dubai after a minimum of 12 months, subject to performance, research output, and business requirements. Our quantitative research function will scale internationally as the firm expands.
Why Join Us
- Build systematic trading strategies in digital assets markets where alpha opportunities are structurally richer and less competed than traditional asset classes.
- Full-cycle ownership from research to production — no handing off models to a separate implementation team.
- Access to institutional-grade data infrastructure, analytics platforms, and exchange connectivity from day one.
- Collaborative culture that values rigour, intellectual honesty, and evidence-based decision-making over hierarchy.
- Competitive compensation with performance-linked profit sharing directly tied to strategy P&L.
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