Quantitative Analyst, Portfolio Engineering & Analytics
As one of the world’s leading independent global investment firms, Invesco is dedicated to rethinking possibilities for our clients. By delivering the combined power of our distinctive investment management capabilities, we provide a wide range of investment strategies and vehicles to our clients around the world. If you're looking for challenging work, intelligent colleagues, and exposure across a global footprint, come explore your potential at Invesco.
What’s in it for you?
Our people are at the very core of our success. Invesco employees get more out of life through our comprehensive compensation and benefit offerings including:
Flexible paid time off
Hybrid work schedule
401(K) matching of 100% up to the first 6% with a discretionary supplemental contribution
Health & wellbeing benefits
Parental Leave benefits
Employee stock purchase plan
About the Team / Department:
Responsible for developing and systematizing multi-asset investment frameworks for our solutions orientated clients, as part of the Invesco Solutions group which manages assets across several systematic and quantitative strategies. The team supports strategic asset allocation, portfolio construction and advisory, leveraging latest cloud, optimization, and visualization technologies. Responsible for Invesco’s flagship cloud-based and client facing, portfolio construction and analytics platform – Invesco Vision®.
About the Role:
As part of the portfolio engineering & analytics team, you will work alongside some of the best quantitative researchers and technologists to help evolve our investment capabilities. You will be responsible for performing research, managing data, and developing APIs that will be used to power the investment process along with our flagship portfolio construction and analytics platform – Invesco Vision®. The role will allow for significant opportunities for creativity and innovation.
Responsibilities of the Role:
Helping construct multi-asset and single asset portfolios using advanced asset allocation and portfolio construction techniques
Building high performance computing algorithms, infrastructure and APIs
Researching and building prediction and forecasting methods based on both classical statistical techniques as well as ML based techniques including Generative AI
Helping translate investment frameworks from various segments of the market (i.e. cash flow driven investing, liability driven investing, insurance capital efficient portfolio construction) into tangible investment and client engagement tools
Leveraging advanced optimization techniques to create optimal portfolio solutions for internal and external stakeholders
Employing multi-period simulation tools to project and optimize performance in the context of flows and optionality
Integrating third party risk models in various portfolio construction exercises
Designing and maintaining procedures and tools that make data management and research more efficient
Working closely with other quantitative and technology teams in the firm in leveraging best practices from a financial theory and technological perspective.
Formulating new ideas for research that will help enhance frameworks and tools
Following academic research and industry trends to constantly incorporate best practice
Requirements of the Role:
- Advanced degree in quantitative disciplines such as engineering, finance, operations research, or computer science is required
- Solid understanding of standard financial engineering techniques
- Excellent knowledge of statistics and optimization
- Strong Python programming expertise, including experience with FastAPI for building RESTful services, Numba for high-performance computing, and deploying solutions on cloud environments (AWS, Azure, or GCP)
- Strong background in technical infrastructure development, Git, and cloud technologies is advantageous
- Researching, implementing, and integrating Large Language Models (LLMs) and conversational AI technologies to develop intelligent client engagement tools and chatbots will be beneficial
- Experience managing and manipulating large data sets (preferably in SQL)
- Strong ability to learn and translate abstract principles into systematic algorithmic representations
- Some experience using third party risk models such as BarraOne or Axioma will be a plus
- Progress towards CFA designation preferred
The salary range for this position in Boston is $120,000 - $135,000 / year. The total compensation offered for this position includes salary and incentive pay and will vary based on skills, experience and location.
Full Time / Part Time Full time Worker Type Employee Job Exempt (Yes / No) Yes Workplace ModelPursuant to Invesco’s Workplace Policy, employees are expected to comply with the firm’s most current workplace model, which as of October 1, 2025, includes spending at least four full days each week working in an Invesco office. This reflects our belief that spending time together in the office helps us build stronger relationships, collaborate more easily, and support each other’s growth and development.
The above information on this description has been designed to indicate the general nature and level of work performed by employees within this role. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications required of employees assigned to this job. The job holder may be required to perform other duties as deemed appropriate by their manager from time to time.
Invesco's culture of inclusivity and its commitment to diversity in the workplace are demonstrated through our people practices. We are proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender, gender identity, sexual orientation, marital status, national origin, citizenship status, disability, age, or veteran status. Our equal opportunity employment efforts comply with all applicable U.S. state and federal laws governing non-discrimination in employment.
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