Institutional Equity Division - Vice President, Quantitative Researcher – Algorithmic Trading (Mumbai)

Overview

Join Morgan Stanley's Equities Quantitative Research team in Mumbai to develop quantitative models, trading strategies and technology supporting MSET's algorithmic trading products.

What you'll do

• Research and develop quantitative techniques to improve the performance, execution quality and trading outcomes of MSET's algorithmic trading products.
• Apply concepts from high-frequency trading and market making to algorithmic execution, including short-horizon price prediction, order placement, liquidity capture, fill probability, queue dynamics and adverse selection.
• Analyze large-scale full-depth order book and order-level (Level 3) market data to understand market behaviour and identify opportunities to improve trading decisions.
• Develop quantitative models, signals and features using order flow, liquidity, cross-asset information and other market data to forecast short-horizon market behaviour and improve execution decisions.
• Apply rigorous statistical techniques including time-series analysis, statistical inference and predictive modelling to evaluate research hypotheses and identify robust effects.
• Design and implement high-fidelity, event-driven simulations using historical order-level market data to evaluate trading strategies under realistic conditions, including appropriate modelling of queue position, fill behaviour, latency and transaction costs.
• Conduct cross-asset research, incorporating information from multiple instruments and order books to improve trading decisions and execution performance.
• Develop rigorous methodologies for measuring execution quality and trading performance, including transaction-cost analysis and evaluation against relevant execution benchmarks, while accounting for latency, market impact, transaction costs, adverse selection and simulation bias.
• Apply appropriate out-of-sample testing and validation methodologies to distinguish robust signals from overfitting and simulation artefacts.
• Translate successful research into high-performance implementations suitable for latency-sensitive production environments.
• Profile and optimize performance-critical code for latency, throughput and computational efficiency.
• Work closely with quantitative researchers, electronic traders and technology teams globally to take research from hypothesis and simulation through to production.

What you'll need

• 3+ years of professional quantitative R&D experience in high-frequency trading, electronic market making, algorithmic trading or a closely related systematic trading environment.
• Experience applying market microstructure concepts to real-world trading problems.
• Strong programming skills in a performant, low-latency language such as C++, Java or Rust, including experience developing efficient, performance-critical software.
• Strong proficiency in Python or a comparable quantitative research environment for data analysis, modelling and rapid research iteration.
• Experience working with full-depth / Level 3 order book data and order-level market events, including analysis of detailed order-book state and dynamics.
• Experience developing or using event-driven market simulation and backtesting frameworks for high-frequency trading or execution research.
• Strong quantitative and statistical modelling skills, including experience with time-series analysis, statistical inference, predictive modelling and empirical research.
• Experience designing rigorous experiments and applying out-of-sample validation to evaluate predictive signals and guard against overfitting.
• Degree in Computer Science, Mathematics, Statistics, Physics, Engineering, Financial Engineering or another highly quantitative discipline.

Nice to have

• Experience researching algorithmic execution, smart order placement, liquidity capture, execution optimization or transaction-cost analysis.
• Experience applying techniques developed in an HFT or electronic market-making environment to broader algorithmic trading problems.
• Strong understanding of electronic market microstructure, including order-book dynamics, matching mechanics, queue position, liquidity, fill behaviour, adverse selection and market impact.
• Experience developing and evaluating quantitative trading strategies using cross-asset and cross-instrument information.
• Strong knowledge of modern C++, algorithms, memory management, concurrency and performance optimization.
• Experience working with very large event-level datasets and high-performance simulation or research infrastructure.

Details

• Location: Mumbai, India.