Assistant Quant Trading Jobs in Kentucky (NOW HIRING)

Careers / Quant Research Engineer, Derived Data Products

We are looking for a Quant Research Engineer to design and prototype the next generation of derived market data products. This is a hybrid role spanning quantitative research, algorithm design, and light engineering. You will work directly with our raw market data—trades, quotes, fundamentals, and alternative datasets—to create clean, consistent, statistically robust derived datasets used by sophisticated trading, risk, and analytics teams.

This is not a traditional "quant trading" role, and it's not a pure engineering role. You will own the methodology, math, and research rigor behind the datasets we ship.

If you love breaking down messy real-world market data, designing elegant algorithms, and turning research into usable data products, this role is for you.

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 & Qualifications

• Strong quantitative background (math, statistics, physics, CS, engineering, or related field).
• Deep understanding of market data structure and microstructure: trades, quotes, NBBO, order book dynamics, price formation, volatility, liquidity.
• Fluency in designing statistical and algorithmic transformations of time-series data.
• Ability to break down noisy real-world data and rebuild reliable, stable, well-defined derived metrics.
• Comfort writing Python, SQL, and simple scripts for prototyping and testing (AI can assist; your domain judgment is what matters).
• Ability to clearly 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 schemes, Bayesian methods, or factor construction is a plus.
• Experience working with large-scale tick data or historical market datasets is a plus.
• Exposure to production engineering concepts (PRs, CI, code review), though deep engineering expertise is not required.

About Massive

At Massive we are on a mission to help developers build the future of fintech. We are committed to democratizing access to the world's financial market data and enabling developers to build the future of fintech. Join us and be part of a team that is revolutionizing the way we interact with money and value.

Massive is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. We strictly prohibit and do not tolerate harassment or discrimination against employees, applicants, or any other covered persons because of race, color, religion, creed, national origin or ancestry, ethnicity, sex, gender, gender identity, age, physical or mental disability, citizenship, sexual orientation, past, current or prospective service in the uniformed services. To request a reasonable accommodation, please email careers@massive.com .

Benefits for full time offers from Massive include, but are not limited to, comprehensive medical plans, 401(k), and unlimited time off. When determining a candidate’s compensation, we consider a number of factors including skillset, experience, job scope, and current market data.

Modernizing Wall St.

Reimagining financial market data for the 21st century.