Remote Employee BPO has an outstanding opportunity for you!
Role: Quantitative Analyst (Financial Markets)
Compensation: Competitive Basic Salary
Bonus: 5,000 Perfect Attendance
Benefits: HMO plus 2 Free Dependents, P1,500 Rice Allowance, Night Differential (If applicable).
Work Schedule: 5-day work week, 2pm to 10pm Philippine time
Work Setup: WFO or WFH
Job Summary:
Introduction: The scope of work for the
Quantitative Analyst role in the financial markets outlines the responsibilities, objectives, and deliverables associated with developing algorithmic trading strategies and quantitative models. The primary objective is to leverage data analysis and mathematical modeling techniques to drive informed investment decisions and optimize trading performance.
Objectives: Develop and implement algorithmic trading strategies to capitalize on market inefficiencies and generate alpha. Conduct quantitative research and analysis to identify patterns, trends, and opportunities in financial markets. Build and validate quantitative models for risk management, portfolio optimization, and performance attribution. Collaborate with traders, portfolio managers, and other stakeholders to enhance trading strategies and drive investment returns. Stay abreast of market developments, industry trends, and regulatory changes to inform quantitative modeling approaches.
Job Duties andResponsibilities:
- Quantitative Research and Analysis: Conduct in-depth quantitative research to identify trading opportunities and develop predictive models. Utilize statistical techniques, machine learning algorithms, and time-series analysis methods to analyze financial data. Identify and validate alpha signals across various asset classes, including equities, fixed income, currencies, and derivatives.
- Algorithmic Trading Strategy Development: Design and implement algorithmic trading strategies to execute trades efficiently and minimize market impact. Optimize trading algorithms for performance, risk management, and compliance with regulatory requirements. Backtest trading strategies using historical data to assess performance and refine model parameters.
- Risk Management and Performance Monitoring: Develop risk models to measure and manage portfolio risk exposure, including value-at-risk (VaR) and stress testing. Monitor trading performance and conduct post-trade analysis to evaluate strategy effectiveness and identify areas for improvement. Collaborate with risk management and compliance teams to ensure adherence to regulatory guidelines and risk limits.
- Stakeholder Collaboration: Work closely with traders, portfolio managers, and investment teams to understand their objectives and requirements. Provide quantitative support and insights to facilitate decision-making and optimize portfolio construction. Communicate findings and recommendations effectively to both technical and non-technical stakeholders.
- Deliverables: Algorithmic trading strategies developed and implemented across asset classes. Quantitative models for risk management, portfolio optimization, and performance attribution. Research reports, whitepapers, and presentations summarizing key findings and insights. Documentation of model development processes, methodologies, and assumptions.
Qualifications:
- Education: Bachelor's degree in Finance, Economics, Statistics, Computer Science, or a related field. A master's degree or professional certifications (e.g., CFA, FRM) are a plus.
- Experience: Background in data analysis, preferably within the financial markets.
- Technical Skills: Proficiency in data analysis tools and software, such as Python, R, SQL, Excel, and data visualization tools (e.g., Tableau, Power BI).
- Knowledge: Strong understanding of financial markets, instruments, and trading mechanisms.
- Analytical Skills: Excellent problem-solving skills with the ability to analyze complex datasets and provide meaningful insights.
- Communication: Strong written and verbal communication skills, with the ability to present findings clearly and concisely.
- Attention to Detail: High level of accuracy and attention to detail in data analysis and reporting.
