Director, Quantitative Trading & Execution Optimization

Fidelity; United States; On-site; USD 170,000 - 185,000; Full time

Job summary

Fidelity is seeking a senior financial engineer to generate insights from historical and real-time data using Python and KDB/Q, building cost models and conducting transaction cost analysis in US equity markets.

You will develop optimization frameworks and indicators to enhance execution quality and scalability across client objectives, leveraging ML and cloud-based AWS for research and deployment.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, IT or related field plus 6 years in Financial Engineering or similar.
• Alternative: Master’s degree plus 2 years in transaction cost analysis in US markets.
• Experience with KDB/Q and Python for historical trading data analysis.
• Experience designing and evaluating execution platforms and models.

Responsibilities

• Leads research on experimental design and analytical controls for platform improvement.
• Defines performance frameworks to evaluate systematic trading platforms.
• Analyzes trading data to identify opportunities, reduce costs, improve outcomes.
• Develops methods to assess market conditions’ impact on platform performance.
• Translates research findings into scalable platform improvements via Agile processes.
• Evaluates execution quality and post-trade trends to guide methodological changes.
• Applies ML models to optimize execution algorithms and minimize market impact.
• Collaborates with trading, order flow, and technology teams to improve best-execution.

Skills

ML algorithms

KDB/Q

Python

AWS

scikit-learn

Vowpal Wabbit

Education

Bachelors in CS/Engineering/IT

Master's in related field

Tools

KDB/Q

Python

AWS

Vowpal Wabbit

scikit-learn

Job description

Fidelity is seeking a senior financial engineer to generate insights from historical and real-time data using Python and KDB/Q, building cost models and conducting transaction cost analysis in US equity markets.

You will develop optimization frameworks and indicators to enhance execution quality and scalability across client objectives, leveraging ML and cloud-based AWS for research and deployment.