Senior Quantitative Research Analyst - Equity Signals, Hybrid

Massachusetts Financial Services Company; Boston (MA); On-site; USD 158,000 - 193,000; Full time

Benefits offered by this job

Retirement match 15%

Tuition reimbursement

Education assistance

Parental leave

Wellness programs

Job summary

MFS is seeking a quantitative researcher to join a team advancing stock selection models and portfolio construction. You will collaborate with portfolio managers and fundamental researchers to implement rigorous quantitative analyses and deploy production code.

The role emphasizes strong programming skills in Python/Matlab/R, SQL, and experience with equity portfolio construction. A graduate degree and familiarity withBloomberg or FactSet are preferred.

Qualifications

• Minimum 5 years of relevant industry experience.
• Detail-oriented, able to work with minimal supervision.
• Strong written and verbal communication skills.
• Experience with quantitative stock selection research.
• Proficiency in Python, Matlab and R.

Responsibilities

• Lead independent research projects to evaluate stock selection factors and improve models.
• Collaborate with analysts and PMs to support a ~$27bn AUM portfolio.
• Contribute to the research agenda by attending conferences and monitoring literature.
• Write production code to enhance research and portfolio management processes.
• Serve on Global Sector Teams providing quantitative analysis for backtests and construction analysis.

Skills

Python

Matlab

R

SQL

C#

Education

Graduate education in relevant field

Tools

Bloomberg

FactSet

Job description

MFS is seeking a quantitative researcher to join a team advancing stock selection models and portfolio construction. You will collaborate with portfolio managers and fundamental researchers to implement rigorous quantitative analyses and deploy production code.

The role emphasizes strong programming skills in Python/Matlab/R, SQL, and experience with equity portfolio construction. A graduate degree and familiarity withBloomberg or FactSet are preferred.