Job details

Quant Research Internship -- Feature Analysis

Job Description: Quantitative Research Intern -- Rates

Blockhouse's success stems from our groundbreaking approach to quantitative finance, blending sophisticated mathematical models with robust financial strategies to redefine market dynamics. We are seeking a Quantitative Research Intern -- Rates to transform raw data into predictive features that enhance trading strategies and execution models.

Key Responsibilities:

Feature Extraction and Signal Development

Design and develop informative signals with predictive power over financial variables, particularly in fixed income markets.
Analyze and transform complex datasets to generate features relevant for trading execution, liquidity risk monitoring, and market making.

Data Labeling and Weighting

Apply advanced techniques to label and weight data for feature generation.
Identify patterns in trading behavior, such as multi-leg trade execution (e.g., curve trades, basis trades, butterflies), and translate them into actionable features.

Classifier and Model Input Design

Use state-of-the-art methodologies to create classifiers and evaluate feature importance for predictive models.
Develop libraries of features that can be applied across multiple trading scenarios and asset classes.

Advanced Visualization

Create compelling visualizations to convey the insights and predictive power of engineered features to quantitative researchers and strategists.

Research and Innovation

Stay informed on the latest developments in information theory, signal extraction, and feature importance techniques, applying them to practical problems in quantitative finance.

Ideal Candidate Profile:

Educational Background

Currently pursuing or having completed a Bachelor’s, Master’s, or PhD in Computer Science, Financial Engineering, Mathematics, Data Science, or related quantitative fields.
Bachelor’s students with a strong willingness to learn and work hard are encouraged to apply.

Technical Expertise

Proficient in Python and familiar with libraries/tools for data processing and feature engineering.
Hands-on experience with machine learning frameworks and feature importance techniques.

Domain Knowledge

Strong understanding of fixed income markets and trading dynamics, particularly in relation to multi-leg trades.
Familiarity with transaction cost analysis and liquidity risk factors.

Analytical Skills

Exceptional problem-solving abilities and a detail-oriented approach to transforming data into meaningful features.

Communication and Collaboration

Capable of articulating complex ideas clearly and collaborating with quantitative researchers and strategists.

Why You Should Join Us:

Innovative Environment

Work with a forward-thinking team that leverages advanced feature engineering to drive innovation in quantitative finance.

Expert Team

Collaborate with top-tier talent in finance and technology, pushing the boundaries of predictive analytics and financial modeling.

Professional Growth

Develop expertise in feature engineering, signal extraction, and advanced visualization techniques.

Compensation

Equity-only compensation that reflects the value of your contributions.

Work Hours

A part-time role with 20-30 hours per week, offering flexibility and immediate start dates.

For International Students:

At Blockhouse, we value talent from across the globe and support international students through e-verification, CPT/OPT documentation, and flexible international payment arrangements.

If you’re passionate about transforming raw data into actionable insights and eager to apply your feature engineering skills to solve complex financial challenges, join us at Blockhouse and shape the future of finance.

What You Should Know About Quant Research Internship -- Feature Analysis, Blockhouse

Are you excited by the world of quantitative finance? Blockhouse is on the lookout for a passionate Quant Research Intern specializing in Feature Analysis to join our dynamic team in New York City. As a Quantitative Research Intern -- Rates, you’ll dive headfirst into the fascinating realm of financial data, where your role will be crucial in transforming raw datasets into predictive features that bolster our trading strategies. You’ll design and develop informative signals, analyze complex financial variables, and leverage advanced techniques for feature generation, particularly within fixed income markets. Your innovative insights could lead to significant advancements in liquidity risk monitoring and market-making efficiency. The ideal candidate will possess a solid educational background in fields such as Computer Science, Mathematics, or Data Science and demonstrate proficiency in Python, with hands-on experience in machine learning and feature engineering. You’ll have the chance to collaborate with a talented team that thrives on pushing boundaries, utilizing cutting-edge methodologies to extract and visualize insights. With a part-time role of 20-30 hours per week, this internship offers the flexibility you need while providing you with valuable exposure to the financial technology ecosystem. If you are prepared to transform analytical skills into actionable outcomes and delve into the latest trends in quantitative finance, Blockhouse is the place for you to progress your career convincingly. We pride ourselves on our diverse environment and welcome international students with supportive arrangements to make your journey effortless. Join us and shape the future of finance together!

Frequently Asked Questions (FAQs) for Quant Research Internship -- Feature Analysis Role at Blockhouse

What are the main responsibilities of a Quant Research Intern -- Rates at Blockhouse?

As a Quant Research Intern -- Rates at Blockhouse, your main responsibilities include designing and developing predictive signals, analyzing complex financial datasets, applying advanced techniques for data labeling, and creating classifiers for various trading scenarios. You'll work closely with the team to transform data into actionable insights, particularly within the fixed income markets.