Employer Industry: Financial Services

Why consider this job opportunity:
- Innovative environment at the forefront of financial technology
- Opportunity to work alongside industry experts and thought leaders
- Vibrant company culture that promotes career development and continuous learning
- Flexible part-time role requiring 20-30 hours per week
- Unique compensation structure with equity-only compensation
- Immediate start available for qualified candidates

What to Expect (Job Responsibilities):
- Develop data-driven models for measuring slippage, market impact, and execution efficiency
- Apply causal inference techniques to assess the impact of different execution strategies
- Conduct research on optimal execution models, including implementation shortfall and order placement strategies
- Use advanced calculus and machine learning to improve execution performance
- Work with large-scale trading data to backtest execution models and evaluate performance

What is Required (Qualifications):
- Strong foundation in calculus, stochastic processes, probability theory, and optimization
- Experience with causal inference frameworks and econometrics in financial modeling
- Understanding of trade execution strategies (VWAP, TWAP, IS) and price impact models
- Strong Python skills, including experience with NumPy, Pandas, and SciPy
- Ability to synthesize and implement findings from execution research papers

How to Stand Out (Preferred Qualifications):
- Familiarity with algorithmic execution frameworks and liquidity dynamics
- Experience handling large financial datasets and running data-driven experiments
- Knowledge of reinforcement learning and stochastic optimization for trade execution strategies

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