Data Scientist at Hedge Fund Guide (Updated for 2024)

Hedge funds currently manage over $5 trillion in assets and are one of the most attractive alternative investments for high-net-worth individuals. These companies invest in many areas and use complex strategies to maximize returns, regardless of market conditions.

Hedge fund managers are required to make important investment decisions regularly, but this is increasingly difficult in an ever-widening investment landscape accompanied by tons of data. This is why data scientists have become critical to the operations of these firms.

In this guide, we dive into the data scientist position at hedge funds. We explain the role they play in these organizations, the skills and qualifications you need to become one, the companies hiring for this position, and the kind of salary you could earn in this career path.

Hedge funds have gained a reputation as some of the best-paying employers for data scientists. What kind of work justifies paying an entry-level data scientist more than senior data scientists in other industries?

Hedge Fund Basics

Hedge funds take money from private investors and invest it with the goal of outperforming average market returns while also hedging the fund against market risks. To achieve this, they put the liquid assets into different investments, including stocks, currencies, derivatives, real estate, and commodities. This alone complicates investment decisions.

However, hedge fund managers can also employ a wide range of strategies, including:

• Long/short equity
• Short-only
• Merger arbitrage
• Market neutral
• Global macro

Some firms, called multi-strategy funds, employ multiple strategies at the same time. Since hedge funds try to take advantage of market conditions to maximize returns, they need data scientists to continually analyze relevant data to identify emerging opportunities and risks in the firm’s areas of interest. This also helps them decide the right strategies to employ.

Thanks to technological trends, what constitutes relevant data to hedge funds has a much wider scope. A data scientist at a hedge fund may be tasked with analyzing market data, financial data, social media sentiment, economic indicators, web traffic, satellite imagery, etc.

Specific Roles of Data Scientists at Hedge Funds

The specific roles of a hedge fund data scientist depend on the company, but they can be broadly grouped as generating accurate predictions on the backend and communicating insights to stakeholders on the front end. Specific activities a data scientist may undertake at a hedge fund include:

• Exploratory Analysis: This helps the fund to know more about the state of the market and involves examining data to identify correlations, trends, outliers, or anything else that may impact the company’s investments or strategy.
• Predictive Modeling: Data scientists can be asked to build statistical and machine-learning models that can predict market movements, asset prices, and other financial metrics.
• Risk Management: Hedge fund data scientists help to identify and quantify risks that come with different assets and strategies. They can also identify areas of risk concentration in the fund’s portfolio.
• Strategy Development: This involves using data analysis and modeling to develop or improve formulas used in the company’s investment strategy.
• Communication: Creating presentations, reports, and other visualizations to communicate findings to portfolio managers, risk managers, executives, and other decision-makers.

Breaking into the world of data science without at least a bachelor’s degree is an uphill task. One survey of LinkedIn data scientist profiles estimates that 76.7% of them have master’s or PhD degrees.  Data scientists with only a bachelor’s degree made up another 19.8% of these profiles.

Working as a data scientist at a hedge fund is an advanced role, and having a graduate degree will be an advantage, if not a requirement. However, the type of degree is also important.

Many data science jobs don’t explicitly state the desired degrees. However, based on the job profile, you’re likely to fare better with a degree in data science, statistics, computer science, quantitative finance, engineering, and similar fields. These degrees are more likely to equip you with the skills needed for this role. Degrees in finance and business also carry weight.

There are hedge fund data scientist positions targeting individuals with as little as one year of experience. However, the bulk of these jobs are intended for candidates with more experience working with data and sometimes with an emphasis on financial data.

To be a strong candidate for these positions, consider starting with an internship or another role in finance. A junior data scientist or an analyst job in a related field is also a good starting point. The goal is to get practical experience solving real problems using data science and improving skills relevant to the job, such as programming, data manipulation, and machine learning. Becoming familiar with financial markets and the data tools used in that ecosystem is also important.

• Prior knowledge/experience: If you’re coming from a degree that works with intermediate to advanced mathematics, this can be helpful for a career transition. Note: certain fields like statistics and probability, discrete math, and linear algebra may be more applicable than physics, calculus, or accounting.
• Business sense: A business-related degree or relevant experience can provide an advantage for switching roles. Economics and quantitative finance topics are particularly relevant for hedge fund data scientists.

Note: hard science degrees aren’t necessarily a guaranteed path into data science. If you’re coming from more health-related coursework, shifting to a data science role can be challenging if you don’t have a solid technical foundation.

Yes, it’s possible to transition from another career path and become a data scientist at a hedge fund. However, this will be easier for some than others. Someone already working in business and finance can transition more easily than someone coming in from an unrelated industry.

Degrees that offer exposure to intermediate and advanced mathematics, statistics and probability, and linear algebra will be helpful, as will degrees in business, finance, and economics.

While there’s no specific degree required for most data science roles, some degrees will be more helpful than most. Data science, computer science, quantitative finance, and engineering degrees provide a strong background in computing, statistics, data visualization, and complex financial models (helpful for FinTech environments).

The required skills for a hedge fund data scientist vary depending on the company, but certain skills are considered essential to these positions, including:

• SQL: SQL is the standard language for querying relational databases, and data scientists at hedge funds work with such databases regularly. Our SQL learning path covers a range of easy to advanced topics plus practice questions.
• Programming: For a hedge fund data scientist role, proficiency in at least one programming language is necessary. Python is commonly used for advanced data analysis, building models, and automation, but R is also suited to some roles. It’s also important to become familiar with tools such as NumPy and pandas. Check out our Python learning path to find out more.
• Data Visualization: Visual communication is a key part of a data scientist’s job at a hedge fund. Python and R libraries such as Matplotlib and dashboard-building tools such as Tableau are used to create eye-catching reports, presentations, and dashboards.
• Statistics and Probability: A strong foundation in statistics and probability will help you build predictive models and come up with data-driven insights. If this is an area you struggle in, IQ’s Statistics and AB Testing course and the Probability course can help you get up to speed on the fundamentals.
• Machine Learning: Machine learning has become a key part of the decision-making process in the finance world. A hedge fund data scientist should be able to build ML models to predict trends, optimize resource use, and automate tasks. Find out more about these applications through our Machine Learning and Modeling learning path.
• Financial Knowledge: Good background knowledge in trading, investment strategies, and financial markets will help you to apply data science in a manner befitting the needs of a hedge fund. At the very least, you should be familiar with their basics.

• SQL: Data scientists in hedge funds handle relational data all the time, and knowing how to query is a very important skill. Our SQL learning path covers a range of easy to advanced topics, along with practice questions.
• A programming language of your choice: Being proficient in at least one programming language is essential. Certain languages are better for certain careers, and, for data science in particular, Python is one of the best choices. R is also useful if you want to learn something other than Python. Learning Python allows you to use essential mathematical tools like NumPy, Pandas, and visualization libraries, as well as automation and scraping. For more help learning Python, check out our Python learning path
  
• Data Visualization: Learning how to use visualization tools in Python or libraries in R (like Matplotlib, Seaborn, or ggplot2) is essential for communicating with your team and making data-driven decisions. Tableau is another powerful tool used for creating dynamic and interactive dashboards.
• Statistics & Probability: A strong foundation in descriptive/inferential statistics, hypothesis testing, probability distributions, and more are important for data science roles. These skills will be helpful for predictive modeling and developing data-driven insights. Interview Query has a Statistics and AB Testing course and a Probability course to jumpstart your data science journey.
• Data Structures and Algorithms: Start learning about basic data structures like arrays and linked lists. You can then proceed to abstract data structures, like deques, trees, and graphs. For algorithms, learning sorting algorithms, graph algorithms, and asymptotic analysis will be helpful.
• Machine learning: Creating machine learning models is a major part of working as a data scientist in a hedge fund. The models you create will be used to predict trends, optimize resources, automate tasks, and much more. To learn more about machine learning, check out our Machine Learning and Modeling learning path.
• Financial Knowledge: Understanding financial markets, trading, and investment strategies will be crucial to applying data science within a hedge fund context. You don’t need to be a full-fledged expert, but you should be familiar with at least the basics.

Listings for hedge fund data scientists may be less common than others, but there are plenty of companies you can apply to, including big-name firms such as:

• UBS: UBS is a big name in the banking industry. UBS Asset Management is also one of the most significant hedge funds in the world, with a reach that extends to virtually all parts of the globe. This means there is plenty of work to keep data scientists busy at this company.
• Dimensional Fund Advisors (DFA): DFA applies both academic research and experience to come up with its investment strategies. Company scientists use decades worth of data to understand the factors that result in higher returns.
• **London Stock Exchange Group (LSEG):** LSEG specializes in providing data on financial markets to various players, including hedge funds. As a data scientist at this company, your analytics could end up being used by more than one hedge fund.

A data science career at a hedge fund may start with a junior role. Candidates for these positions will typically have some years of work experience. However, there are companies with special programs for recruiting fresh, talented graduates.

The next step will be mid-level, followed by the senior data science role, with each step bringing more responsibility and independence. Senior data scientists often specialize in specific industries or areas of investment, e.g., energy, manufacturing, private equity, and fixed income. Some senior data scientists may also transition into managerial positions.

Many hedge fund data scientists start their careers in different industries, but their expertise allows them to transition to senior positions in hedge funds. A shining example of this is Claudia Perlich, who started out as a member of the research staff at IBM before taking on roles in other companies, including Dstillery, and eventually joining the finance world as a senior data scientist at Two Sigma.

Hedge fund data scientists are some of the best-paid data scientists in the world. An entry-level position in the US pays between $120,000 and $350,000. This huge range arises from different factors such as city and whether you’re working on the banking side or for the hedge fund, with the latter usually paying more.

Data scientists with 3 to 6 years of experience can make between $300,000 and $600,000, and those with 6 to 10 years could earn as much as $850,000 annually.

Hedge funds are responsible for ensuring the fortunes entrusted to them yield good returns. This requires them to make sense of a vast amount of data, and they rely on data scientists for this. To qualify for hedge fund data scientist positions, having the right educational background and relevant skills is essential. Experience in finance or data science is also crucial. With the right qualifications, you could help make decisions that impact billions of dollars in investments.

At Interview Query, we empower our users to have what it takes to land these plum positions in data science. Our interview questions and individual company guides can help you prepare for your next hedge fund data science interview. Our coaches can also guide you on how to better handle interviews while you get more practice using our AI interviewer or mock interview features. You can also check out our job board to see which companies are hiring for these positions.

Landing one of these high-paying positions is the dream for many data scientists, and we hope this guide will help you achieve that goal.