Principal Quantitative Developer, Investments Technology (Asset Allocation)

Job Locations US-MA-Boston
ID
2024-62876
Position Type
Full-Time
Minimum Salary
USD $124,900.00/Yr.
Maximum Salary
USD $216,100.00/Yr.
Typical Starting Salary
$170,000-$180,000
Flexible Time Off Annual Accrual - days
20

Pay Philosophy

The typical starting salary range for this role is determined by a number of factors including skills, experience, education, certifications and location. The full salary range for this role reflects the competitive labor market value for all employees in these positions across the national market and provides an opportunity to progress as employees grow and develop within the role. Some roles at Liberty Mutual have a corresponding compensation plan which may include commission and/or bonus earnings at rates that vary based on multiple factors set forth in the compensation plan for the role.

Description

 

We are seeking a highly skilled and motivated Principal Quantitative Engineer with expertise in asset allocation to join our prestigious Investment firm. As a Quantitative Engineer, you will work closely with portfolio managers, traders, and other members of the investment team to develop, implement, and deploy to production quantitative models that support research, portfolio management and asset allocation decisions.

 

Note:  This Boston-based role has a hybrid work arrangement (2 days per week in office).

 

Responsibilities: 

  • Develop and enhance quantitative models and tools to support the asset allocation process, utilizing statistical and econometric techniques.
  • Contribute to the development and implementation of the quant library, supporting both existing and new models and analytics.
  • Develop expertise in the LMI data ecosystem and create and automate data pipelines for portfolio and market data across public and private markets.
  • Support existing applications/dashboards in production and actively contribute to the design and implementation of new ones.
  • Collaborate with the Macro and Cross Asset Research team to conduct rigorous analysis of financial data, including historical market trends, macroeconomic indicators, and other relevant factors, to identify and exploit investment opportunities.
  • Collaborate with the Quantitative Solutions team to research, evaluate, and implement advanced portfolio optimization techniques.
  • Stay up to date with the latest industry trends, developments, and best practices, actively contributing to the continuous improvement of investment processes and methodologies.
  • Collaborate with other team members on various quantitative research projects, fostering a culture of learning and innovation by sharing insights and contributing to the collective knowledge base.

 

Qualifications

  • Expertise in statistics, econometrics, and quantitative analysis, with a solid understanding of financial markets, investment instruments, and portfolio theory.
  • 5 to 8 years of experience working with analytical models and partnering with investment professionals
  • Demonstrated experience in developing and implementing quantitative in an asset management or similar environment.
  • Proficiency in programming languages such as Python with the ability to write efficient and robust code to process and analyze large financial datasets.
  • Strong knowledge of optimization techniques for portfolio optimization.
  • Familiarity with market data platforms, financial databases, and data manipulation techniques.
  • Excellent problem-solving skills, with the ability to think critically, independently, and act with minimal handholding.
  • Effective communication skills, with the ability to clearly articulate complex ideas and analysis to both technical and non-technical stakeholders.
  • Strong attention to detail, organization, and the ability to manage multiple tasks and priorities in a fast-paced environment.
  • Master's or Ph.D. (or equivalent experience) in a quantitative field such as Mathematics, Statistics, Econometrics, Financial Engineering, or a related discipline.

 

At Liberty Mutual Investments Technology, we offer a stimulating and collaborative work environment that fosters professional growth and encourages innovative thinking. Join our team of exceptional professionals as a Quant Engineer, and contribute to our continued success in delivering outstanding results for our clients through cutting-edge asset allocation strategies.

 

If you are a self-driven, analytical thinker with a passion for quantitative finance and asset allocation, we invite you to apply for this exciting opportunity.

 

About Us

At Liberty Mutual, our purpose is to help people embrace today and confidently pursue tomorrow. That's why we provide an environment focused on openness, inclusion, trust and respect. Here, you'll discover our expansive range of roles, and a workplace where we aim to help turn your passion into a rewarding profession.

Liberty Mutual has proudly been recognized as a "Great Place to Work" by Great Place to Work® US for the past several years. We were also selected as one of the "100 Best Places to Work in IT" on IDG's Insider Pro and Computerworld's 2020 list. For many years running, we have been named by Forbes as one of America's Best Employers for Women and one of America's Best Employers for New Graduates as well as one of America's Best Employers for Diversity. To learn more about our commitment to diversity and inclusion please visit: https://jobs.libertymutualgroup.com/diversity-inclusion

We value your hard work, integrity and commitment to make things better, and we put people first by offering you benefits that support your life and well-being. To learn more about our benefit offerings please visit: https://LMI.co/Benefits

Liberty Mutual is an equal opportunity employer. We will not tolerate discrimination on the basis of race, color, national origin, sex, sexual orientation, gender identity, religion, age, disability, veteran's status, pregnancy, genetic information or on any basis prohibited by federal, state or local law.

Fair Chance Notices

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