Portfolio Optimization Strategist

5 days ago

Bengaluru, India Carrin & Stone Advisory Full time

Company Description

Carrin & Stone Advisory is a SEBI-registered investment advisor and research analyst firm specializing in next-generation, quantitative investment solutions. Our adaptive, data-driven model portfolios span mutual funds, direct equity, smart beta, and thematic strategies, meticulously tailored to align with unique financial goals and risk appetites. Harnessing the power of machine learning and sophisticated quantitative models, our services are designed to perform with discipline and transparency in dynamic markets. We are committed to delivering investment solutions that empower our clients and help achieve wealth aspirations.

Role Description

This is a full-time remote role for a Portfolio Optimization Strategist. The Portfolio Optimization Strategist will be responsible for analyzing and optimizing investment portfolios, developing quantitative models, back-testing strategies, and leveraging machine learning for portfolio enhancements. The role includes regular monitoring of portfolio performance, conducting risk assessments, and generating reports. The strategist will collaborate with the research team to identify opportunities for portfolio improvement and ensure alignment with client goals and risk appetites.

Qualifications

  • Experience in Investment Analysis, Portfolio Management, and Quantitative Modelling
  • Proficiency in Machine Learning, Statistical Analysis, and Algorithm Development
  • Skills in Data Analysis, Financial Research, and Risk Management
  • Strong problem-solving skills and meticulous attention to detail
  • Excellent communication skills, including the ability to present complex information clearly
  • Ability to work independently and maintain accountability in a remote work environment
  • Advanced degree in Finance, Mathematics, Statistics, or related field; relevant certifications (e.g., CFA, FRM) are a plus
  • Experience in the investment advisory industry is preferred

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