Position Description: 

Designs and implements systematic trading platform in US equity markets that integrate real-time intelligence into optimized trading strategies. Creates models and optimization engines driving equity trading algorithms -- VWAP, LSA, IS, and Dark -- to improve execution quality and scale of deployment given varying client objectives. Creates analytical insights based on historical data using Python and KDB/Q, and determines optimization strategies that improve implementation short falls and liquidity capture of trading algorithms. Develops analytical systems to produce real-time insights and mitigate risk based on data in Amazon Web Services (AWS) services and streaming data in Kafka. Designs A/B test aiming to gain market insights and drive optimization. Conducts performance reviews, continually validates models in production, examines A/B test results, and proposes new strategies to improve performance of algorithms. Collaborates with product team to create product roadmap, develop quantitative requirements to production systems, create prototypes to drive innovation, and grow technology team with competency to drive speed of implementation. 

Primary Responsibilities: 

Establishes performance benchmarks and analytics to evaluate systematic trading platforms. 

Proposes models and logics to improve trading algorithms by decomposing performance indicators and examining system logs. 

Conducts transaction cost analysis and continuous evaluation of client orders. 

Applies statistics and machine learning techniques to create liquidity, price and performance prediction in trading applications. 

Integrates in agile squad to propose, implement and test quantitative solutions in production systems. 

Works side-by-side with client facing team to create trading solutions for clients. 

Establishes thought leadership via market microstructure structure and systematic trading research. 

Works closely with product developers, traders, clients, and software developers to deploy electronic trading products. 

Researches and designs trading systems, including algorithmic optimal trading strategies, order placement strategies, smart order routing, and portfolio trading. 

Optimizes model parameter selection by using both real-world trading data and simulated environments to improve performance of the trading strategies. 

Develops and implements advanced simulation techniques to model dependencies between financial instruments, improve accuracy in simulations, and reduce variance. 

Collaborates with client-facing teams to create trading solutions for clients. Creates tools, prototypes, and simulation engines to streamline processes and test new ideas. 

Builds and deploys machine learning models for price, liquidity and performance prediction in trading applications. 

Conducts market structure studies and provides valuable market insights to businesses. 

Participates in creation of next generation brokerage products. 

Education and Experience: 

Bachelor’s degree (or foreign education equivalent) in Computational Finance, Financial Engineering, Financial Mathematics, Data Science, or a closely related field 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 or Python. 

Or, alternatively, Master’s degree (or foreign education equivalent) in Computational Finance, Financial Engineering, Financial Mathematics, Data Science, or a closely related field 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 or Python. 

Skills and Knowledge: 

Candidate must also possess: 

Demonstrated Expertise (“DE”) designing and implementing logics to improve systematic trading algorithms (VWAP, POV, Dark, LSA, or IS), examining raw logs and conduct transaction cost analysis using Python or KDB/Q; deploying systematic trading systems and logic within Amazon Web Services (AWS), streaming data in Kafka, and signal stream-processing in Flink. 

DE developing, analyzing, and evaluation machine learning algorithms, volatility forecasting, volume profile generation, and liquidity signals using Python, R, hmmlearn, or Pytorch. 

DE creating simulation engines using Aliasing Algorithm, Antithetic Variables, and Stratification; and designing programs to reflect real-world price dynamics and market microstructures (structure of exchanges and trading venues, price discovery process, determinants of spreads and quotes, intraday trading behavior, and transaction costs) using KDB/Q or Python. 

DE solving financial market allocation issues using Linear Programming, Quadratic Programming, Stochastic Optimization, and CVXPY (a Python package for convex programming). 

#PE1M2 

Company Overview

Fidelity Investments is a privately held company with a mission to strengthen the financial well-being of our clients. We help people invest and plan for their future. We assist companies and non-profit organizations in delivering benefits to their employees. And we provide institutions and independent advisors with investment and technology solutions to help invest their own clients’ money.

Join Us

At Fidelity, you’ll find endless opportunities to build a meaningful career that positively impacts peoples’ lives, including yours. You can take advantage of flexible benefits that support you through every stage of your career, empowering you to thrive at work and at home. Honored with a Glassdoor Employees’ Choice Award, we have been recognized by our employees as a top 10 Best Place to Work in 2024. And you don’t need a finance background to succeed at Fidelity—we offer a range of opportunities for learning so you can build the career you’ve always imagined.

Fidelity’s hybrid working model blends the best of both onsite and offsite work experiences. Having the majority of our associates work onsite is important for our business strategy and our culture. We also value the benefits that working offsite offers associates. Most roles listed as Hybrid will require associates to work onsite all business days of every other week in a Fidelity office. This does not apply to roles listed as Remote or Onsite.

At Fidelity, we value honesty, integrity, and the safety of our associates and customers within a heavily regulated industry. Certain roles may require candidates to go through a preliminary credit check during the screening process. Candidates who are presented with a Fidelity offer will need to go through a background investigation, detailed in this document, and may be asked to provide additional documentation as requested. This investigation includes but is not limited to a criminal, civil litigations and regulatory review, employment, education, and credit review (role dependent). These investigations will account for 7 years or more of history, depending on the role. Where permitted by federal or state law, Fidelity will also conduct a pre-employment drug screen, which will review for the following substances: Amphetamines, THC (marijuana), cocaine, opiates, phencyclidine.

We invite you to Find Your Fidelity at fidelitycareers.com.

Fidelity Investments is an equal opportunity employer. We believe that the most effective way to attract, develop and retain a diverse workforce is to build an enduring culture of inclusion and belonging.

Fidelity will reasonably accommodate applicants with disabilities who need adjustments to participate in the application or interview process. To initiate a request for an accommodation, contact the HR Accommodation Team by sending an email to accommodations@fmr.com.