Systematic Equity Active, Research Analyst, Officer

Overview

Senior Quantitative Researcher in the Systematic Equities Active (SE Active) team at State Street Investment Management, contributing to next-generation quantitative active equity strategies.

What you'll do

• Conceptualize and develop alpha strategies using optimization, machine learning, deep learning, NLP, data science techniques, and economic insights.
• Back-test and evaluate investment strategies, data vendors, alternative datasets, and predictive signals to drive innovation and enhance alpha generation.
• Manipulate, engineer, and analyze large structured and unstructured datasets to conduct robust and bias-aware research simulations.
• Partner with portfolio managers, researchers, and data scientists globally to transition research ideas into scalable investment solutions.
• Explore and apply emerging AI, machine learning, and advanced analytics techniques to investment research challenges.

What you'll need

• Advanced degree in Computer Science, Statistics, Mathematics, Engineering, Physics, or a related quantitative discipline from IITs, NITs, IISc, or other leading institutions.
• 8 plus years of relevant experience.
• Strong knowledge of probability, statistics, machine learning, pattern recognition, NLP, and time-series analysis.
• Excellent programming skills in Python, R, MATLAB, or similar scientific computing environments.
• Experience working with large-scale structured and unstructured datasets.
• Knowledge of database technologies and data engineering concepts.
• Strong interest in financial markets and quantitative investing.
• 2-6 years of industry experience in quantitative research, data science, machine learning, or quantitative development roles.
• Creative, self-motivated, intellectually curious, and detail-oriented with high standards of integrity and quality.
• Strong communication skills and ability to collaborate effectively in a global team environment.

Nice to have

• Familiarity with quantitative investing, factor investing, portfolio construction, or investment theory.
• Experience with distributed computing and large-scale data processing technologies such as Spark, Databricks, Hadoop, Hive, or SparkSQL.
• Working knowledge of Linux environments.
• Experience with deep learning frameworks such as TensorFlow or PyTorch.
• Experience with cloud computing platforms and modern data engineering workflows.
• Experience with data visualization and analytical tools such as Tableau or Power BI.