Roles we hire for

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Data Science

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Quantitative Researcher

A Quantitative Researcher conducts data analysis and statistical modeling to inform decision-making.

What is Quantitative Research?

Quantitative research is all about the numbers, and uncovering insights and macro trends from large collections of data. Quantitative Researchers analyze data from a variety of sources – from behavior to user demographics to weather to the global economy – and help companies understand which metrics to pay attention to when making business decisions. Quantitative Researchers may also often be found at data companies, like companies that are mining or selling data.

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What is the typical background of a Quantitative Researcher?

A Quantitative Researcher will often have an advanced background in statistics, economics or applied mathematics, as much of their day-to-day work encompasses complex algorithms and modeling. A Bachelor’s degree, Master’s degree, or even in a PhD in a field like physics, data science or advanced mathematics, may also be beneficial.

Quantitative Researchers are often found in the financial or risk management industries, where data is used to make significant decisions – and can also impact a company’s bottom line. More recently, blockchain companies have also started to hire individuals for these roles too.

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What are some of the typical responsibilities of a Quantitative Researcher?

To design study requirements: In addition to being able to conduct research, a Quantitative Researcher will know how to design an entire study: like formulating a hypothesis, identifying control variables, and crafting a robust methodology/selection process.
To model data: Quantitative Researchers will need a thorough understanding of how to create algorithms and model various, often complicated scenarios, which can have many different variables.
To identify data trends that are statistically significant: Simply because a metric or trend appears to exist doesn’t mean that it is statistically significant – and Quantitative Researchers can help organizations learn which numbers demonstrate real, actual trends, and which numbers are not statistically sound. Simply put - a Quantitative Researcher can help companies focus on the right numbers.

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What are some of the skills a successful Quantitative Researcher should have?

Experience with data sensitivity and/or security: A Quantitative Researcher should understand how to properly house sensitive or private data, especially if working in a highly regulated industry like finance, risk management or healthcare where data may need to be especially safeguarded.
Ability to work cross-functionally: Quantitative Researchers will often need to work with different teams throughout an organization, like data, analytics, marketing, product management, and finance to provide guidance and results.
Experience with Machine Learning: It may be helpful for Quantitative Researchers to understand machine learning and natural language processing, in order to develop data models.
Knack for storytelling and analysis: Quantitative Researchers will often need to present their data and findings. Being able to tell a story from the numbers - for example, being able to show stakeholders what the risk level is behind a certain trade, or identifying the latest B2B advertising trends amongst a sea of variables – can be an invaluable skill.

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What are some of the programming languages a Quantitative Researcher needs to know?

Python, C++ or C sharp, Linux and MatLab and natural language processing can all be important for a Quantitative Researcher. Data access tools, like SQL and Hive, may also be recommended for Quantitative Researchers (for several examples, check out this list fromMeta).

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