Quantitative Financial Analyst
UmiUni.com is seeking a highly analytical and research-driven Quantitative Financial Analyst to support the development, testing, and evaluation of quantitative trading strategies. The successful candidate will work closely with the trading software engineering and research teams to analyze financial markets, build quantitative models, backtest investment strategies, and produce data-driven insights for portfolio and risk management decisions. This role requires strong quantitative skills, financial market knowledge, programming ability, and the discipline to evaluate strategies using rigorous statistical and risk-adjusted performance methods. The Quantitative Financial Analyst will play a key role in helping UmiUni.com evaluate trading opportunities, improve strategy performance, manage portfolio risk, and build a disciplined quantitative investment research process. The ideal candidate combines financial intuition, statistical rigor, programming ability, and a strong interest in systematic investing.
Responsibilities
- Research, design, and evaluate quantitative trading strategies across equities, ETFs, derivatives, and other financial instruments.
- Conduct statistical analysis on historical market data, including price, volume, volatility, macroeconomic indicators, and alternative datasets.
- Build and maintain financial models for alpha generation, portfolio construction, risk management, and performance attribution.
- Backtest trading strategies and evaluate results using metrics such as CAGR, Sharpe ratio, Sortino ratio, maximum drawdown, volatility, win rate, and risk-adjusted return.
- Identify market regimes, factor exposures, and potential sources of systematic return.
- Work with trading software engineers to translate research ideas into production-ready trading systems.
- Monitor live and simulated strategy performance and identify performance degradation, risk drift, or abnormal trading behavior.
- Prepare research reports, investment memos, and internal documentation explaining model assumptions, methodology, findings, and risks.
- Support portfolio optimization, position sizing, risk control, and capital allocation decisions.
- Stay informed on developments in quantitative finance, machine learning, financial markets, macroeconomic trends, and trading technology.
Requirements
- Bachelor’s or Master’s degree in Finance, Economics, Mathematics, Statistics, Computer Science, Engineering, Data Science, or a related quantitative field.
3-5 years of work experience is preferred.
- Strong understanding of financial markets, trading strategies, portfolio theory, and risk management.
- Proficiency in Python for financial data analysis, modeling, and backtesting.
- Experience with libraries such as pandas, NumPy, scikit-learn, statsmodels, matplotlib, yfinance, QuantConnect, or similar tools.
- Strong knowledge of statistics, probability, regression analysis, time-series analysis, and hypothesis testing.
- Familiarity with factor models, momentum strategies, mean reversion, volatility modeling, regime detection, or machine learning-based trading strategies.
- Ability to work with large financial datasets and clean, transform, validate, and analyze market data.
- Understanding of key performance and risk metrics, including Sharpe ratio, drawdown, beta, alpha, volatility, VaR, and correlation.
- Strong analytical and problem-solving skills with attention to detail and intellectual discipline.
- Ability to clearly communicate quantitative findings to both technical and non-technical stakeholders.
- Self-motivated, research-oriented, and comfortable working in a remote startup-style environment.
Preferred Qualifications
- Experience with algorithmic trading, hedge fund research, asset management, or proprietary trading.
- Knowledge of ETFs, leveraged ETFs, options, futures, or crypto markets.
- Experience with QuantConnect, Alpha Vantage, Interactive Brokers API, Bloomberg, Refinitiv, or other financial data/trading platforms.
- Familiarity with machine learning, reinforcement learning, NLP, or alternative data in financial applications.
- Understanding of market microstructure, execution costs, slippage, liquidity, and transaction cost modeling.
- Experience writing formal investment research reports or strategy documentation.
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