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

Overview

Join Morgan Stanley's Equities Quantitative Research team in Mumbai to research and develop quantitative techniques that improve the performance, execution quality and trading outcomes of 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.