Job Description
Are you a data-driven financial professional ready to leverage the power of equity options markets?
Oakland Quantitative Strategies is seeking a highly analytical Equity Options Data Analyst to join our elite team. In this pivotal role, you will transform complex market data into actionable insights that drive our trading strategies and risk management protocols. If you have a passion for financial mathematics and advanced data engineering, we want to meet you.
Why Join Us?
- Work at the forefront of quantitative finance in the heart of the Bay Area.
- Access to cutting-edge financial data and proprietary technology.
- Competitive compensation package and comprehensive benefits.
- A collaborative environment that values innovation and precision.
As a key member of our analytics team, you will ensure our data pipelines are robust, our models are accurate, and our strategies are ahead of the curve.
Responsibilities
- Advanced Data Processing: Design, develop, and maintain robust ETL pipelines for high-volume equity options data, ensuring accuracy and timeliness for real-time trading systems.
- Volatility Analysis: Analyze historical and implied volatility surfaces to identify market trends, mispricings, and arbitrage opportunities using statistical models.
- Model Support: Collaborate with quants and traders to validate pricing models and backtesting frameworks, providing critical feedback on data quality and anomalies.
- Visualization & Reporting: Create complex, interactive dashboards and reports to communicate market insights, risk metrics, and performance analytics to stakeholders.
- Data Governance: Establish and enforce data quality standards and documentation protocols for all equity options datasets.
- System Optimization: Continuously optimize query performance and database architecture to handle the latency-sensitive nature of options market data.
Qualifications
- Education: Bachelor’s or Master’s degree in Mathematics, Statistics, Physics, Computer Science, or Finance (or equivalent work experience).
- Technical Skills: Strong proficiency in Python (Pandas, NumPy) and SQL for data manipulation and querying.
- Financial Knowledge: Deep understanding of options pricing theory (Black-Scholes, Binomial models), Greeks, and market microstructure.
- Experience: Proven experience working with financial data providers (Bloomberg, Refinitiv, or internal feeds) and data warehousing technologies.
- Analytical Mindset: Ability to troubleshoot complex data issues and derive meaningful conclusions from large, unstructured datasets.
- Communication: Excellent written and verbal communication skills, capable of translating technical findings for non-technical audiences.