About QSentia
QSentia is building an AI-powered investment intelligence platform for family offices, RIAs, wealth managers, hedge funds, brokerages, asset managers, and institutional investors.
Our platform combines proprietary reinforcement learning, adaptive portfolio risk management, explainable AI, model observability, and institutional-grade monitoring across equities, futures, digital assets, and multi-asset strategies.
Unlike traditional investment platforms focused primarily on data or trading signals, QSentia is building the infrastructure professional investors need to evaluate, deploy, monitor, and govern AI-driven investment models.
Our long-term vision is to become the trusted AI infrastructure powering the next generation of investment management.
About the Role
We are seeking a Head of Quantitative Development to lead the development and productionization of QSentia's quantitative investment technology.
This person will sit at the intersection of quantitative research, machine learning, software engineering, portfolio construction, risk management, and execution.
You will be responsible not simply for developing models, but for creating the infrastructure that transforms quantitative research into robust investment systems capable of operating in institutional environments.
This is a founding leadership opportunity for someone who wants to take meaningful ownership of QSentia's quantitative technology and help build the technical foundation of an early-stage institutional fintech company.
What You Will Own
- Lead QSentia's quantitative development organization
- Own the complete research-to-production lifecycle for quantitative models
- Develop and productionize systematic strategies across equities, futures, digital assets, and multi-asset portfolios
- Advance QSentia's reinforcement learning architecture for portfolio allocation, position sizing, and adaptive risk management
- Build institutional-quality backtesting and simulation infrastructure
- Implement walk-forward testing and rigorous out-of-sample validation
- Develop portfolio construction and optimization systems
- Build volatility targeting, exposure management, drawdown controls, and risk-budgeting frameworks
- Develop transaction cost, slippage, liquidity, and market-impact models
- Build execution and order-management logic
- Design broker, exchange, and market-data integrations
- Establish standards for reproducible quantitative research
- Develop frameworks to prevent look-ahead bias, survivorship bias, data leakage, overfitting, and unrealistic execution assumptions
- Develop model performance attribution, observability, and monitoring systems
- Establish processes for detecting model degradation, changing market regimes, and abnormal risk behavior
- Work with engineering leadership to scale quantitative workloads across cloud and distributed infrastructure
- Establish model validation, governance, and production approval processes
- Recruit, mentor, and eventually lead a team of quantitative developers and researchers
Ideal Background
- 10+ years of experience in quantitative finance, systematic trading, quantitative development, financial engineering, or related disciplines
- Experience at a hedge fund, quantitative asset manager, proprietary trading firm, investment bank, market maker, or institutional trading organization
- Exceptional Python skills and experience building production-grade quantitative systems
- Strong understanding of quantitative portfolio management and systematic investing
- Experience developing and operating production trading or investment systems
- Deep understanding of statistics, probability, optimization, time-series analysis, and financial markets
- Experience working with equities, futures, options, digital assets, or multi-asset strategies
- Strong understanding of portfolio construction, risk management, transaction costs, liquidity, and execution
- Experience applying machine learning techniques to financial markets
- Strong software engineering practices and experience working with large-scale financial datasets
- Ability to translate quantitative research into reliable, scalable, and maintainable production systems
- Strong leadership skills with the ability to establish technical standards and build a quantitative organization from the ground up
Highly Preferred
- Reinforcement learning experience
- Deep learning and transformer experience
- Experience with actor-critic methods such as PPO, SAC, or TD3
- Experience developing adaptive portfolio allocation or dynamic position-sizing systems
- Experience building systematic long/short portfolios
- Knowledge of market microstructure and institutional execution
- Experience working with Interactive Brokers, Bloomberg, Polygon, Alpaca, CME, ICE, or other institutional market-data and execution providers
- AWS, cloud infrastructure, and distributed computing experience
- Experience with model observability, monitoring, explainability, and governance
- Master's degree or PhD in Mathematics, Statistics, Computer Science, Physics, Engineering, Financial Engineering, Machine Learning, or another related quantitative discipline
What Success Looks Like
You will transform QSentia's quantitative research into an institutional-grade model development, validation, and deployment engine.
Success means models are not judged solely by backtest returns. They must demonstrate robust out-of-sample performance, controlled drawdowns, realistic transaction costs, explainable risk behavior, reproducibility, scalability, and readiness for deployment within professional investment environments.
You will establish rigorous standards for research, validation, risk management, model monitoring, and production deployment so that QSentia's quantitative technology can meet the expectations of sophisticated institutional investors.
Over time, you will build and lead the quantitative organization responsible for one of QSentia's most important competitive advantages: AI that learns not only where opportunities exist, but how much capital should be put at risk.
Compensation & Equity
This is a founding leadership role and is initially equity-based while QSentia completes its pre-seed financing. The position does not currently include a fixed salary or guaranteed cash compensation.
The successful candidate will be eligible for an equity-based compensation package ranging from 0% to 10%, with the actual allocation determined based on experience, level of commitment, responsibilities, performance, and long-term contribution to QSentia.
Equity may be earned and/or vested against clearly defined milestones, including:
- Successful development and productionization of QSentia's quantitative systems
- Delivery of robust and validated investment models
- Development of scalable quantitative research and backtesting infrastructure
- Improvements in model reliability, risk management, observability, and deployment readiness
- Technical and quantitative leadership
- Recruitment and development of the quantitative team
- Contribution to institutional pilots, partnerships, and commercial readiness
- Overall long-term contribution to QSentia's technology, intellectual property, and company growth
The 0–10% range represents the potential equity opportunity and is not a guaranteed allocation. Final equity, vesting schedules, performance milestones, eligibility requirements, and other terms will be determined through mutual agreement and formally documented.
Following QSentia's pre-seed financing and subject to the company's financial position, the role is expected to transition toward a combination of cash compensation and equity, with the objective of establishing a competitive compensation package for senior quantitative and financial technology leadership.
This opportunity is designed for someone who wants more than a traditional employment role: someone who wants meaningful ownership, the ability to shape the company's quantitative direction, and the opportunity to help build the technology at the core of QSentia.
Pay: From ₹1.00 per hour
Work Location: Remote
