Quant Researcher
Weekday AI
Posted: March 2, 2026
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Quick Summary
A Quant Researcher will analyze large datasets, develop predictive models, and create scalable research pipelines to uncover actionable insights in a fast-paced environment.
Required Skills
Job Description
This role is for one of the Weekday's clients
Min Experience: 0 years
Location: India
JobType: full-time
As a Quantitative Researcher, you will be responsible for analyzing large datasets, developing predictive models, and performing statistical research to uncover actionable insights. You will leverage Python extensively to conduct research, build backtesting systems, automate workflows, and create scalable research pipelines. This position offers hands-on exposure to quantitative finance, algorithmic trading, and advanced data science techniques in a fast-paced environment.
Requirements:
Key Responsibilities
• Conduct quantitative research on financial markets including equities, derivatives, and other asset classes
• Develop and implement statistical and machine learning models using Python
• Design and maintain backtesting frameworks to evaluate trading strategies
• Clean, process, and analyze structured and unstructured datasets
• Perform exploratory data analysis to identify patterns, anomalies, and alpha signals
• Collaborate with trading and engineering teams to translate research into production-ready strategies
• Optimize code for performance, scalability, and reliability
• Document research findings, methodologies, and model performance clearly and concisely
• Stay updated with advancements in quantitative finance, statistics, and machine learning
Required Skills & Qualifications
• 0–2 years of experience in quantitative research, data science, financial engineering, or related field (internships and academic projects included)
• Strong proficiency in Python, including libraries such as NumPy, Pandas, SciPy, scikit-learn, and Matplotlib
• Solid understanding of probability, statistics, linear algebra, and optimization
• Experience with data manipulation, feature engineering, and model validation techniques
• Familiarity with backtesting methodologies and performance metrics (Sharpe ratio, drawdown, volatility, etc.)
• Knowledge of financial markets and instruments is a strong advantage
• Basic understanding of SQL and working with large datasets
• Ability to write clean, efficient, and well-documented code
Preferred Qualifications
• Exposure to time-series analysis and financial modeling
• Experience with machine learning frameworks or deep learning tools
• Familiarity with version control systems (Git)
• Background in mathematics, statistics, computer science, engineering, or quantitative finance
• Participation in Kaggle competitions, research publications, or personal trading projects
What We’re Looking For
• Strong problem-solving and analytical thinking skills
• Intellectual curiosity and passion for data-driven decision-making
• Ability to work independently while collaborating effectively within a team
• Strong communication skills to present technical concepts clearly
• Attention to detail and commitment to research rigor