Quantitative Researcher - Portfolio Optimization - Remote | Radnor, PA, USA

Quantitative Researcher - Portfolio Optimization - Remote {"description": "SCM is committed to a workplace that values and promotes diversity, inclusion and equal employment opportunity by ensuring that all employees are valued, heard, engaged and involved at work and have full opportunities to collaborate, contribute and grow professionally. We are currently seeking a highly driven, well organized, and motivated candidate to join our team. SCM offers the opportunity to work in person, remotely or in a hybrid work environment.

Primary Responsibilities:

• Design and implement multi-period portfolio optimization frameworks incorporating transaction costs, slippage, and other market frictions
• Leverage MOSEK and other optimization solvers to build scalable and efficient models
• Develop and refine intraday trading strategies and execution algorithms
• Monitor and analyze model performance in a live trading environment

Requirements:

• Strong quantitative background (PhD or Master's in Applied Math, Operations Research, Computer Science, or related field)
• Proven experience with MOSEK or other optimization frameworks
• Deep understanding of slippage, transaction cost modeling, and intraday trading
• Familiarity with real-time data processing and execution systems
• Programming skills in Python and/or C++
• Experience integrating optimization routines in production trading systems The base pay for this position is anticipated to be between $150,000 and $300,000 per year.

The anticipated annual base pay range is current as of the time this job post was generated. This position is eligible for other forms of compensation and benefits, such as a bonus, health and dental plans and 401(k) contributions, which includes a discretionary profit sharing program. An employee's bonus and related compensation benefits can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications and experience.", "salary_raw": "Row(double=None, string='USD 150,000.00 - 300,000.00 per year')"}