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Intermediate Quantitative Analyst

Job in Boston, Suffolk County, Massachusetts, 02298, USA
Listing for: RiverSource Life Insurance Company
Full Time position
Listed on 2026-10-19
Job specializations:
  • Finance & Banking
    Financial Analyst, Data Scientist
Salary/Wage Range or Industry Benchmark: 140000 - 160000 USD Yearly USD 140000.00 160000.00 YEAR
Job Description & How to Apply Below
## Intermediate Quantitative Analyst Apply:
Hybrid:
Boston, Massachusetts:
Full time:
Posted Today:
End Date:
November 20, 2026 (30+ days left to apply):
R26 _4163
** About Our Company
** Working at Columbia Threadneedle Investments you'll find growth and career opportunities across all of our businesses.

We're intentionally built to help you succeed. Our reach is expansive with a Columbia Threadneedle Investments global team of 2,300 people working together. Our capability is diverse with more than 550 investment professionals sharing global perspectives across all major asset classes and markets. Our clients have access to a broad array of investment strategies, and we have the capability to create bespoke solutions matched to clients' specific requirements.

Our unwavering focus on our clients and strong financial foundation connects each of our enterprise businesses — Ameriprise Financial, Columbia Threadneedle Investments and River Source Insurance and Annuities.
** Job Description
** Columbia Threadneedle Investments is a global investment firm with teams across North America, Europe, and Asia Pacific. Our Quantitative Equity Research team is looking for a curious, driven Quantitative Analyst to join us. We build and maintain a suite of stock selection models that power both systematic and fundamental strategies throughout the firm. In this role, you will research new signals, develop alpha models, and refine our portfolio construction methods.

You will also play a hands-on part in shaping the research and analytics platforms we rely on every day and share ownership of the models we run in production. Working closely with fundamental analysts and portfolio managers across the globe, you will exchange ideas and insights while broadening your expertise across a wide range of investment approaches. If you enjoy solving investment problems with data and want to grow alongside a collaborative team, we would love to hear from you.
** Responsibilities:
**** Research and Model Development
*** Contribute to research projects spanning idea generation, data preparation, statistical analysis, and actionable recommendations
* Evaluate innovative techniques for extracting tradable signals from large datasets
* Develop alpha models and refine portfolio construction methodologies
** Production and Monitoring
*** Share ownership of implementing and maintaining the team's models in production
* Enhance the team's research infrastructure and analytics platforms
* Monitor and analyze factor and model performance, and conduct due diligence on model output
** Communication and Collaboration
*** Collaborate with portfolio managers and fundamental analysts across the firm, exchanging ideas, sharing regular performance insights, and responding to their inquiries
* Present research findings to key stakeholders and develop thought leadership content
*
* Qualifications:

**** Experience and Technical Skills
*** Bachelor's degree in Mathematics, Statistics, Finance, Economics, or a related field
* Minimum 3 years of direct experience in quantitative investment research, including hands-on development and testing of stock selection signals or alpha models
* Solid grasp of equity markets, factor investing, and portfolio construction, coupled with strong intellectual curiosity
* Deep knowledge of statistical modeling, econometric analysis, and machine learning, with demonstrated ability to apply these techniques to investment problems
* Strong programming proficiency in Python, R, or similar languages, including experience working with large financial datasets
* Advanced problem-solving and critical thinking skills, with the ability to frame research questions, challenge assumptions, and separate robust signals from noise
** Professional…
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