Quantitative Researcher -Data Infrastructure & Signal Development

Millennium•New York, NY

11d•$150,000 - $200,000•Onsite

About The Position

We are seeking a versatile quantitative researcher with strong data engineering skills to join a newly formed systematic equities pod focused on intraday mean reversion and market microstructure strategies. You will be responsible for building and maintaining the research data infrastructure, and for developing and testing trading signals using statistical and machine learning methods. This role combines data engineering rigor with quantitative research creativity. You will work directly with the Portfolio Manager to turn raw market data into actionable trading signals.

Requirements

Bachelor's or Master's degree in Mathematics, Statistics, Physics, Computer Science, Financial Engineering, or a related quantitative field
3+ years of experience in a quantitative research or data-intensive role in a buy-side or sell-side financial firm
Strong programming skills in Python with deep proficiency in Polars, Pandas, NumPy, and SciPy
Solid understanding of statistical methods: regression, time-series analysis, hypothesis testing, cross-validation
Familiarity with equity markets, market microstructure, and intraday trading dynamics
Strong data engineering instincts: schema design, data quality, pipeline reliability
Detail-oriented with strong problem-solving skills and intellectual curiosity
Excellent communication skills and ability to work In a small, fast-paced team

Nice To Haves

Experience with tick-level or order-book data analysis
Familiarity with Apache Arrow, Parquet, and columnar data formats
Experience with kdb+/q for time-series data
Familiarity with Al-assisted development tools (Cursor, Claude Code)

Responsibilities

Build and maintain the research data pipeline: ingestion, cleaning, normalization, and storage of tick-level and minute-bar equity data
Design and Implement a high-performance research environment using Python, Polars for interactive analysis of large datasets
Develop, backtest, and validate intraday alpha signals using statistical methods and classical machine learning (Lasso, Ridge, tree-based models)
Perform feature engineering on market microstructure data: order flow, spread dynamics, volume profiles, and cross-sectional patterns
Build automated backtesting frameworks with realistic transaction cost modeling and slippage estimation
Collaborate with the C++ developer to publish validated signals into the production trading engine
Monitor live signal performance, detect regime changes, and maintain signal quality over time
Document research findings, maintain reproducible research notebooks, and contribute to the team knowledge base