Quantitative Analyst (736091)

A full-time Quantitative Analyst position is available immediately, in the company's location in Brockton, Massachusetts, reporting to the Investment Strategies and Client Relations Manager at Holmes Investment Management, Inc.

Reporting to the Investment Strategies and Client Relations Manager, the Quantitative Analyst will have the following specific job responsibilities:

1) Utilize quantitative analysis tools to address portfolio construction and optimization, performance measurement, attribution, profit and loss measurement, and pricing models;

2) Provide trade support on a portfolio of option trading accounts, along with professional technical analysis reports on momentum strategies;

3) Optimize portfolio performance and ensure portfolio diversification in trading U.S. Treasuries and long-term equity holdings;

4) Develop mathematical and statistical models and conduct analysis and backtesting to evaluate options trading strategies, including risk management, trading efficiency, and relative

value analysis;

5) Apply mathematical and statistical techniques to analyze implied volatility and dividend yield to determine the risk-reward ratio;

6) Screen potential fixed income securities with proper rating, duration, liquidity, and yield to maturity;

7) Compile market research reports and create a mosaic of market consensus on future path of interest rates;

8) Study the stochastic processes and put-call parity and their applications to stock market and fixed-income market valuations;

9) Develop analytical capabilities and model algorithms using advanced statistical, quantitative, and econometric techniques, such as GARCH models, Monte Carlo simulations, and

Python modules, to conduct algorithmic trading; and

10) Conduct model validation and apply independent algorithms to help verify the results of analytical systems.

Work Hours: 9:00 AM - 5:00 PM

A minimum of one (1) year of employment, project, or internship experience in financial quantitative analysis.

Required employment, project, or internship experience must include the following:

(1) A minimum of one (1) year of demonstrated experience in financial modeling and valuation techniques, calculating metrics such as Net Present Value (NPV), cash flows, and weighted

average cost of capital (WACC);

(2) A minimum of one (1) year of demonstrated experience in using Python modules, such as NumPy and pandas, to perform modeling and statistical analysis;

(3) A minimum of one (1) year of demonstrated experience in employing stochastic processes and volatility models, utilizing simulations such as Monte Carlo and Finite Difference Grid;

(4) A minimum of one (1) year of demonstrated experience in statistical and regression analysis in finance, including hypothesis testing, computing R-squared values, and standard error;

(5) A minimum of one (1) year of demonstrated experience in employing risk analysis and risk management concepts, using simulation techniques to investigate parameters such as

systematic risk, option maturity, and money management strategies;

(6) A minimum of one (1) year of demonstrated experience in quantitative portfolio analysis and security selection, evaluating performance using key metrics such as drawdown, annual

profit, profit-to-drawdown ratio, profit factor, and Sharpe ratio;

(7) A minimum of one (1) year of demonstrated experience in algorithmic/automated strategy design and evaluation, including development of automated programs to support trading

strategies, using programing languages including Python and C++;

(8) A minimum of one (1) year of demonstrated experience in collecting, cleaning, and analyzing financial data using Pandas and AI methods.

Bachelor's Degree in Financial Mathematics, Financial Engineering, Information Technology Engineering, or a related field (foreign equivalent is acceptable)

Please copy and paste your resume in the email body (do not send attachments, we cannot open them) and email it to candidates at placementservicesusa.com with reference #736091 in the subject line.

Thank you.