Principal Quant Developer

Note: Fidelity will not provide immigration sponsorship for this position.

The Quantitative Research & Investing Technology (QRIT) team within Fidelity's Asset Management Technology group is seeking a highly motivated and curious Principal Quantitative Developer. In this role you will contribute to a dynamic and fast-paced development team supporting researchers in prototyping and delivering new systematic investment strategies. You will provide high impact solutions on various projects including alpha research, portfolio construction, and risk management. Your technology knowledge covers a broad spectrum of technologies, including 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 will possess: A Bachelor's degree in Computer Science, Financial Engineering, Information Technology, Information Systems, Mathematics, Physics, Statistics, Engineering, or a closely related field and six (6) years of experience as a Senior 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 Lead Quantitative Development or similar role. This experience should include building high-quality, robust, and efficient systems and solutions for financial investment decisions, utilizing Python, PL/SQL databases, and quantitative techniques.   The Expertise and Skills You Bring   Core Engineering Expert in Python with experience across the development stack (full stack) Exposure to object-oriented programming (OOP) and design patterns Experience in at least one unit testing framework and understanding of test-driven development (TDD) concepts and methodologies Working knowledge of R is a plus   Quantitative & Domain Knowledge Strong, demonstrable knowledge of mathematics, statistics, and quantitative finance (core to this role) Deep understanding of quantitative techniques and methods, statistics and econometrics including probability, linear regression and time series data analysis Analyze and design systems to implement quantitative models for systematic financial investments using Python, including time series forecasting models, multi-asset class portfolio construction strategies, risk management tools, alpha research, and simulation-based algorithms Domain knowledge in either equities, fixed income or alternative asset classes Proven track record of delivering production quant solutions in a systematic investing or trading environment Experience with industry-scale optimization libraries (e.g., Gurobi, CPLEX, Axioma, SciPy) and portfolio construction / optimization is a strong plus Progress towards CFA (or equivalent) a plus   Data & Infrastructure Skilled in SQL databases (Oracle); Snowflake, NoSQL, or Graph databases a plus Skilled in batch and API technologies: such as batch scheduling (using Autosys and Airflow) and creating REST APIs (using FAST API and Flask) Proven ability to construct and manage robust data pipelines and event-driven workflows Proven expertise in system design and cloud architecture on AWS, leveraging resources including Lambda, S3, EKS, and EC2   DevOps & CI/CD Experience in containerization with Docker; orchestration with Kubernetes a plus Implement CI/CD pipelines (using Linux and Jenkins), code versioning using GitHub Experience in Infrastructure as Code methodologies for consistent and scalable infrastructure management Familiarity with observability and production support (logging, tracing, monitoring, alerting) a plus   MLOps & AI (Preferred) Operationalizing ML models and pipelines on AWS using modern MLOps principles, including SageMaker (training, deployment, model registry, monitoring) and Bedrock (foundation model access,