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

Company Profile

Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, investment management and wealth management services. The Firm's employees serve clients worldwide including corporations, governments, and individuals from more than 1,200 offices in 43 countries.

As a market leader, the talent and passion of our people is critical to our success. Together, we share a common set of values rooted in integrity, excellence and strong team ethic. Morgan Stanley can provide a superior foundation for building a professional career - a place for people to learn, to achieve and grow. A philosophy that balances personal lifestyles, perspectives and needs is an important part of our culture.

Department Profile

From global institutions to hedge funds, investors come to Morgan Stanley for sales, trading, and market-making services in almost every type of financial instrument in all the world’s financial markets. Morgan Stanley professionals use our network and technology to provide liquidity and sophisticated analysis, to manage risk and execute reliably in the fast-changing markets.

Morgan Stanley’s Institutional Equity Division (IED) is a world leader in the origination, distribution and trading of equity, equity-linked and equity-derivative securities. Our broad and deep client relationships, market-leading platform and intellectual insights enable us to be a world-class service provider to our clients for their financing, market access and portfolio management needs.

Morgan Stanley's Equities Quantitative Research (QR) team develops quantitative models, trading strategies and technology that support the Firm's electronic trading business.

We are looking for an experienced Quantitative Researcher to join the team in Mumbai, focusing on research and development for Morgan Stanley Electronic Trading (MSET)'s algorithmic trading products .

The role will apply techniques and insights from high-frequency trading, market making and electronic market microstructure to improve algorithmic trading performance and execution outcomes. The successful candidate will combine rigorous quantitative research with strong software engineering skills and experience conducting realistic, high-fidelity simulations using full-depth, order-level market data .

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.

Qualifications

• •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.

Preferred Qualifications

• •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.

The Candidate

The ideal candidate combines the mindset of a quantitative researcher with strong software engineering ability . They will bring a deep understanding of how electronic markets behave at the order-book level and be able to translate that understanding into practical improvements in algorithmic trading.

We are particularly interested in candidates who can apply the sophisticated modelling, forecasting and simulation techniques used in high-frequency and market-making environments to improve execution decisions, trading performance and client outcomes across Morgan Stanley's electronic trading platform .

WHAT YOU CAN EXPECT FROM MORGAN STANLEY:

At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best an