Job Description

Job Description:

The Role

As an experienced leader, you will spearhead the execution of quantitative research initiatives, proficiently navigating the Software Development Lifecycle (SDLC) with a comprehensive full-stack approach. Your technology knowledge covers a broad spectrum of technologies, including R, Python, and PL/SQL databases, positioning you as a full-stack software engineer who capitalizes on enterprise technology. You are committed to constructing high-quality, scalable, robust, resilient and efficient analytical and software solutions that propel investment processes forward. You excel in analyzing information to determine, recommend, and plan the installation of new systems or modifications to existing ones. You lead the software engineering team and collaborate with various investment teams on projects encompassing portfolio construction, risk management, alpha research, and the development of new quantitative product software. Your leadership ensures the delivery of innovative solutions within the ever-evolving realm of investment technology.

You will possess:

• A Bachelor’s degree in Computer Science, Financial Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and six (6) years of experience as a Principal Quant Developer or similar role.
• Alternatively, a Master’s degree (or equivalent foreign education) in the same fields, accompanied by four (4) years of experience as a Quantitative Development Director or similar role.
• This experience should include building high-quality, robust, and efficient systems and solutions for financial investment decisions, utilizing R, Python, PL/SQL databases, and quantitative techniques.

In this role, you have demonstrated expertise and expected to:

• Implementing and maintaining cutting-edge investment tools and strategies developed by quantitative researchers.
• Analyze and design systems to implement quantitative models for systematic financial investments using R and Python. This includes developing time series forecasting models, multi-asset class portfolio construction strategies, risk management tools, alpha research, and simulation-based algorithms to build investment strategies.
• Design and build data analytics life cycles for internal and vendor-based financial systems such as MSCI Barra and Morningstar.
• Build algorithms for large-scale data processing and investment risk calculations using distributed computing and parallel processing techniques.
• Expert in Mathematical Optimization techniques, including LP, NLP, MIP, and familiarity with optimization tools, e.g., Axioma, CPLEX, or Gurobi.
• Create dashboards for alpha and beta performance analysis using Python Dash, R Shiny, and Tableau.
• Design and implement large-scale data pipelines, models, and interfaces on Amazon Web Services (AWS) and on-premise computing environments.
• Build automated diagnostic reporting processes for model management.
• Develop methodologies, data models, and performance-tuned PL/SQL queries to build data pipelines for standardization, cleansing, and aggregating data.
• Design and implement highly scalable production-ready systems that comply with software engineering practices using DevOps tools.
• Perform Continuous Integration (CI) and Continuous Deployment (CD) pipelines (using Linux and Jenkins), code versioning (using GitHub), batch scheduling (using Autosys and Airflow), and create REST APIs (using FAST API and Flask). Additionally, create executables using AWS Lambda, S3, and EC2.
• Lead software technology teams, leveraging quantitative development best practices, building teams, acquiring strategic talent, and resource planning.

The Team

Our team is dedicated to the implementation of multi-asset-class quantitative models for systematic financial investments, crafted by ‘our adept quant researchers. These models serve the investment professionals at Fidelity Asset Management, encompassing analysts and portfolio managers across various domains such as equities, fixed income, liquid alternatives, and other asset classes. The scope of our quantitative models is extensive, including alpha signals, risk exposures, and factors integral to portfolio engineering and construction. We cater to a broad array of financial solutions, including separately managed accounts, systematic funds, thematic funds, workplace investing, and more.

Certifications:

Category:

Fidelity’s hybrid working model blends the best of both onsite and offsite work experiences. Working onsite is important for our business strategy and our culture. We also value the benefits that working offsite offers associates. Most hybrid roles require associates to work onsite every other week (all business days, M-F) in a Fidelity office.

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