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# Quantitative Researcher -Data Infrastructure & Signal Development

- **Company:** MILLENNIUM
- **Location:** New York, NY, United States
- **Experience:** Experienced
- **Salary:** $150,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Apache HTTP Server, Big Data, C++ (Programming Language), Computer Programming, Information Engineering, Data Infrastructure, Cursor (Graphical User Interface Elements), Programming Tools, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, NumPy, SciPy, Parquet, Feature Engineering, Pandas, Information Technology
- **Published:** June 10, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=da3aab2398a10d19

## About the Role

Do you have experience in Time series models?, Do you have a Master's degree?, * 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

Preferred Skills / Experience

 * 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)

## Description

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.

Principal 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


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