Quant Research Engineer: Derivative Market Data Architect

Polygon.io, Inc; Northern (KY); Hybrid; USD 130,000 - 190,000; Full time

Benefits offered by this job

Medical plans

401(k)

Unlimited time off

Job summary

Massive is hiring a Quant Research Engineer to design and prototype derived market data products. You will work with raw market data—trades, quotes, fundamentals, and alternatives—to create clean, robust datasets for trading, risk, and analytics teams.

You will own the methodology, math, and research rigor behind the datasets we ship, and prototype algorithms using Python or SQL for validation and performance on large data. This is a hybrid role.

Qualifications

• Strong quantitative background in math, statistics, physics, CS, or engineering.
• Deep understanding of market data structure and microstructure: trades, NBBO, order book dynamics.
• Fluency in designing statistical and algorithmic transformations of time-series data.
• Ability to break down noisy real-world data and rebuild derived metrics.
• Comfort writing Python, SQL, and simple scripts for prototyping and testing.
• Ability to articulate assumptions, methodology, and edge-case behavior in writing.
• Experience in quantitative research or dataset creation at a market data provider, asset manager, hedge fund, or trading firm is a plus.
• Familiarity with smoothing filters, microstructure noise models, interpolation, Bayesian methods, or factor construction is a plus.
• Experience with large-scale tick data or historical market datasets is a plus.
• Exposure to production engineering concepts (PRs, CI, code review).

Responsibilities

• Identify high-value derived datasets by understanding how quants, researchers, and data scientists use market data in their workflows.
• Design and specify quantitative methodologies for new datasets—from statistical assumptions to signal construction to edge-case handling.
• Prototype algorithms using Python or SQL to validate correctness and performance on large datasets.
• Build and document rigorous methodology definitions that customers trust and internal teams can implement.
• Develop robust approaches for data cleaning, normalization, smoothing, interpolation, and event alignment.
• Work directly with raw market microstructure data (trades/quotes/order books) to derive stable, actionable metrics.
• Conduct backtests, stress tests, and statistical validation to ensure each dataset behaves as intended.

Skills

quantitative background

time-series analysis

data science

statistical methods

algorithm design

prototyping

Python

SQL

Tools

Python

SQL

Job description

Massive is hiring a Quant Research Engineer to design and prototype derived market data products. You will work with raw market data—trades, quotes, fundamentals, and alternatives—to create clean, robust datasets for trading, risk, and analytics teams.

You will own the methodology, math, and research rigor behind the datasets we ship, and prototype algorithms using Python or SQL for validation and performance on large data. This is a hybrid role.