# Institutional Equity Division - Vice President, Quantitative Researcher – Algorithmic Trading (Mumbai) at Morgan Stanley

Morgan Stanley is hiring an Institutional Equity Division - Vice President, Quantitative Researcher – Algorithmic Trading (Mumbai) (India). Level rates it Little AI ●○○○; you can [apply on Level](https://jobsbylevel.com/go/b249ba74-7e82-4dc1-bc74-d42133698652?utm_medium=ai-agent).

AI Level 1, AI centrality 0 out of 100. Mumbai, India.

## Details

- Company: [Morgan Stanley](https://jobsbylevel.com/companies/morgan-stanley)
- AI level: AI Level 1 (score 0 out of 100)
- Location: Mumbai, India
- Posted: October 14, 2026
- Apply: https://jobsbylevel.com/go/b249ba74-7e82-4dc1-bc74-d42133698652?utm_medium=ai-agent

## Prepare for this job

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## Description

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

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