Financial Engineer – Entry to Mid Level

Role Description:

The Financial Engineer supports the development and implementation of quantitative models and financial tools used for risk management, pricing, and investment strategy across various asset classes. Working at the intersection of finance, mathematics, and technology, this role involves building, testing, and optimizing algorithms, analyzing financial markets, and developing solutions that enhance trading, hedging, or portfolio management processes. Entry-level candidates may assist with model validation, data analysis, and automation tasks, while mid-level professionals may take on responsibilities such as leading model design, collaborating with traders and analysts, or integrating tools into production systems. The Financial Engineer works closely with cross-functional teams, including risk, research, and software engineering, to ensure models are accurate, efficient, and aligned with business objectives. A strong foundation in quantitative finance, programming, and statistical modeling is essential for success in this role.

Qualifications:

• Bachelor’s or Master’s degree in Financial Engineering, Mathematics, Statistics, Computer Science, Physics, or a related quantitative field
• 1–4 years of relevant experience in financial modeling, quantitative analysis, or algorithm development
• Solid understanding of financial instruments (e.g., derivatives, fixed income, equities) and market structures
• Strong programming skills in Python, R, C++, or MATLAB; experience with SQL and data manipulation tools is a plus
• Knowledge of stochastic calculus, time-series analysis, and numerical optimization techniques
• Familiarity with risk models (e.g., VaR, CVaR), pricing frameworks (e.g., Black-Scholes, Monte Carlo), and backtesting methodologies
• Experience working with large datasets and data visualization tools
• Strong analytical and problem-solving abilities with attention to detail
• Ability to communicate technical concepts clearly to non-technical stakeholders
• Familiarity with financial databases (e.g., Bloomberg, Refinitiv, Quandl) is a plus
• Exposure to machine learning or AI applications in finance is a bonus
• A proactive mindset and ability to work both independently and collaboratively in a fast-paced environment