Loomis Sayles is a performance-driven active asset management company that seeks to identify exceptional investment opportunities on behalf of institutional and retail clients worldwide. We believe active management fueled by proprietary, best-in-class research helps us achieve financial success for our clients. Founded in 1926, Loomis Sayles currently oversees approximately $310 billion in assets under management for global clients spanning more than 50 countries.
We foster a culture of entrepreneurialism, where all employees are empowered and encouraged to develop themselves and their ideas. Our culture centers on our shared IDEALS , the core characteristics of who we aspire to be as employees and an organization.
I NCLUSIVE & DIVERSE - D EDICATED TO TEAMWORK – E XCELLENT – A CCOUNTABLE – L EADERS - S OLUTION-ORIENTED
About The Role
Loomis Sayles is committed to the continual improvement of our performance, processes and people. As part of this commitment, we are looking to hire an enthusiastic and passionate candidate, eager to contribute to our continued success through the following employment opportunity:
You will play a key role in uncovering alpha opportunities across asset classes, leveraging your expertise in machine learning, optimization, and other innovative advanced data-driven techniques. This is a full time role based out of our Boston office. The candidate will be required to follow the firm’s rule on Work from Office (3 days).
About The Team
Loomis Sayles' Systematic Investing Strategies (SIS) team seeks a talented and passionate Quantitative Researcher to join our dynamic group. We are a highly collaborative, data-driven, intellectually rigorous team responsible for coming up with investment strategies, programming those hypothesis into signals, simulating a back-test of the signals, and producing alpha, risk and trading cost forecasts based on the signals to drive trading decisions. We maintain a friendly, team-oriented environment and place a high value on professionalism, attitude and entrepreneurship.
Job Responsibilities
- Craft innovative strategies using advanced mathematical techniques, primarily in derivatives markets (futures, forwards, CDX, swaps)
- Perform exploratory data analysis on structured and unstructured data to identify alpha drivers and build robust signals
- Performing ad-hoc exploratory statistical analysis across multiple large complex data sets
- Explore and integrate text data into your research to drive alpha generation
- Apply machine learning techniques (deep, reinforcement, causal) to build and refine monetization systems for trading signals
- Attend industry events and conferences to stay ahead of the curve and gain local market insights. Contribute to the growth of the Multi-Asset Alpha signal generation team
- Develop and maintain client marketing materials, effectively communicating complex concepts to diverse audiences
Qualifications & Education Requirements
- PhD in a relevant field (Physics, Math, Engineering, Chemistry, Statistics) Candidates with 2-5 years of buy-side or sell side experience from top-tier universities preferred
- Technical expertise: Proficiency in machine learning (deep/reinforcement/causal), linear and non-linear optimization, and Bayesian statistics
- Programming: Strong analytical programming skills, with a preference for MATLAB and Python
- Personal qualities: Highly motivated, detail-oriented, and able to collaborate and communicate effectively across different levels
- Data-driven mindset: Prior experience in a data-driven analytical research environment is a plus
Loomis Sayles Benefit Overview
EEOC and Diversity S tatement
Loomis Sayles is deeply committed to building a diverse and inclusive workforce in which talented individuals can realize their full potential and contribute to our growth and success. Please consider applying for this role even if your work history and skillset doesn’t completely match the job description. We believe creativity, tenacity and humility are as valuable as specific skills that can be practiced and perfected on the job.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, creed, color, gender , age, national origin, religion, sexual orientation, gender identity, status as a vete ran, and basis of disability or any other federal, state or local protected class.
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