Director, Financial Engineering

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

Job Description:

Fidelity will not provide immigration sponsorship for this position.

Position Description :

Generates insights from historical and real-time data using Python and KDB/Q to assess cost of trading, constructing cost models and conduct transaction cost analysis, evaluating execution quality, and developing systematic algorithm selection framework. Designs and advances systematic trading platforms within U.S. equity markets, integrating real-time intelligence into optimized execution strategies. Leads the development of models, performance measurement systems, and analytical frameworks that enhance execution quality, scalability, and performance across diverse client objectives. Develops optimization framework that improve execution quality given flow complexion and dealer competitions in equity and options. Conducts market structure, liquidity, and venue behavior research using public data, leveraging cloud-based virtual machines in Amazon Web Services (AWS) for analysis.

Primary Responsibilities:

• Leads research on experimental design, measurement standards, and analytical controls to drive continuous platform improvement.


• Defines performance frameworks to evaluate systematic trading platforms and inform strategic enhancements.


• Analyzes trading and execution data to identify opportunities, reduce transaction costs, and improve customer outcomes.


• Develops analytical methods to assess the impact of market conditions on platform performance and operational decision-making.


• Translates quantitative research findings into scalable platform improvements through Agile development processes.


• Evaluates execution quality and post‑trade performance trends, recommending changes to methodologies or workflows based on analytical insights.


• Applies statistical and machine learning (ML) models to optimize execution algorithms, enhance trading performance, and minimize market impact.


• Leads research on market structure, routing logic, liquidity dynamics, and client behavior to inform execution strategy.


• Develops prototypes and analytical tools to assess broker performance and generate strategic insights.


• Collaborates with trading, order flow management, and technology teams to strengthen analytical capabilities and improve best‑execution outcomes across asset classes.


• Analyzes decentralized finance (DeFi) protocols and partners with business, product, and technology teams to deliver scalable capabilities within the DeFi ecosystem.

Education and Experience :

• Bachelor's degree in Computer Science, Engineering, Information Technology, Information Systems, Financial Technology, or a closely related field (or foreign education equivalent) and six (6) years of experience as a Director, Financial Engineering (or closely related occupation) performing transaction cost analysis in US markets and analyzing historical trading data using KDB/Q and Python.


• Or, alternatively, Master's degree in Computer Science, Engineering, Information Technology, Information Systems, Financial Technology, or a closely related field (or foreign education equivalent) and two (2) years of experience as a Director, Financial Engineering (or closely related occupation) performing transaction cost analysis in US markets and analyzing historical trading data using KDB/Q and Python.

Skills and Knowledge :

• Demonstrated Expertise developing ML algorithms and quantitative forecasting tools including retail volume forecast, equity contextual trading algorithm selection, and fixed income municipal new issuance pricing using Python, KDB/Q, scikit‑learn (SK Learn), and Vowpal Wabbit; and conducting transaction cost analysis and algorithm selection using KDB/Q, leveraging cloud based virtual machines in AWS for analytical workflows.


• Demonstrated Expertise influencing dealer pricing and execution decisions using optimization models based on linear programming, implemented in KDB/Q and Python; and conducting simulations to project dynamics of market markers under varying market constraints using KDB/Q and python.


• Demonstrated Expertise performing statistical analysis of execution results using hypothesis testing techniques, including t-tests, analysis of variance (ANOVA), and non‑parametric methods including Wilcoxon signed‑rank and Mann‑Whitney U tests; and applying bootstrap resampling techniques to detect information leakage, assess performance differences, and validate cost model stability through confidence intervals.


• Demonstrated Expertise conducting DeFi analysis by examining blockchain ecosystem architecture, gas‑fee economics, and decentralized exchange models including automated market makers (AMMs) and liquidity aggregators; and validating on‑chain data and supporting decentralized finance research through analysis of token issuance mechanisms.

Salary:

Salary: $169,986.00 to $185,000.00/year.

Fidelity's Onsite Working Model