Quantitative Analyst

Job Description

Quantitative Analyst | Franklin Templeton

The Tone:
This is a full-time role at Franklin Templeton, where success is built through powerful partnerships in asset management. The company focuses on building dynamic relationships with clients, understanding their goals, and navigating complex markets using cutting-edge strategies and deep insights. This role is crucial for enhancing the investment process through data-driven insights, portfolio analytics, and tool development, directly contributing to clients’ long-term wealth creation.

The TL;DR
• Role: Full Time
• Type: Full-time
• Pay: $150000–$200000 yearly
• Team: Quantitative Research Team
• Mission: Enhance the investment process by applying data-driven insights, portfolio analytics, and quantitative tool development to support idea generation, portfolio construction, and risk management.
• Tech Stack: SQL, R, Python

What You’ll Actually Do
• Tool Development: Develop and maintain quantitative tools to support fundamental stock selection and portfolio construction processes.
• Portfolio Support: Support portfolio construction decisions through optimization, scenario analysis, and risk-aware frameworks.
• Data Analysis: Analyze alternative and traditional datasets to identify insights relevant to company fundamentals and market behavior.
• Advanced Analytics: Apply natural language processing and machine learning techniques to extract insights from structured and unstructured data.
• Research & Communication: Conduct quantitative research to support client engagement, including marketing materials, presentations, and white papers, translating results into clear narratives.

The Must-Haves
• Background: Bachelor’s or Master’s degree in finance, mathematics, statistics, computing science, or a similar quantitative field.
• Experience: Minimum of 5 years of relevant experience in a quantitative role within asset management, with experience supporting fundamental equity teams or in a hybrid quant/fundamental environment.
• Skills: Knowledge of equity factor models and their practical application, ability to analyze large datasets and translate outputs into actionable ideas, strong written and verbal communication skills.
• Bonus: Proficiency in SQL, R, and Python; experience applying natural language processing and machine learning methods to investment research; in-depth knowledge of risk management and optimization techniques.