In the high-stakes landscape of the finance sector, understanding the role of a data scientist at a hedge fund is critical. Our guide sheds light on how optimizing data-driven business decisions can maximize profits. Due to the sheer scale of these systems, even sub-cent differences can translate into multi-million dollar decisions.
Given the ever-pressing need for speedy and reliable business insights, there’s been a growing demand for hedge fund data scientists.
Hedge funds are high-risk, high-volume environments that utilize the powers of scale, all to “beat the market”. If you plan to change career paths and want to jumpstart your way into a Hedge Fund data science role, this article will be perfect for you.
Hedge fund data scientists specialize in data analysis, machine learning, and statistical modeling to make informed decisions. By analyzing large data sets, these data scientists create models to spot patterns, trends, and anomalies that feed into an information pool to power business decisions.
A hedge fund is an investment vehicle that aims to generate profit through a capital pool (i.e., consolidating funds from many sources and aggregating them as a single product). However, unlike other funds that also pool resources, hedge funds generate higher-risk decisions that, in turn, yield high returns while also creating barriers (or ‘hedges’) against potential losses. Typically, they have a higher barrier for entry and invest in a diversified portfolio, which can include stocks, bonds, and derivatives.
Because of their aggressive policies and strategies, hedge funds need data scientists to generate informed decisions through machine learning models, which enable the identification of trades and investment opportunities at split-millisecond speeds.
Hedge fund data scientists serve in multiple roles depending on the company. They’re involved on the backend by creating models to generate accurate predictions. On the frontend, they communicate reports to stakeholders. The role can be broken down into the following sections:
What does a data science career in a hedge fund look like? Here are some of the numbers to consider:
The answer to this question highly depends on the path you’re coming from, and even then, certain variables will affect your eligibility. Some factors to consider include:
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.
The data science industry is notorious for having many professionals with graduate degrees. According to this study that looked at LinkedIn profiles, over half of the analyzed data scientists holding at least a master’s degree, with around 21% possessing a Ph.D. As such, for more advanced positions (including hedge fund roles), a graduate degree is preferred if not explicitly required.
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).
While the full scope of a data science role can vary by the company, some essential skills remain universal, including:
Testing your knowledge is an essential part of learning, as it helps you evaluate your understanding of a topic and identify areas for improvement. This can be done through self-assessments, peer evaluation (mock interviews), and/or solving interview questions and problem sets.
When preparing for an interview, it’s important to understand that interview questions often test not only your knowledge but also your problem-solving skills, ability to think critically, and soft skills for effective communication. Practicing questions is one of the best ways to tackle these areas. Some topics you may see in a hedge fund data science interview include:
annual_payments table, answer the following questions and output each of them as a table.
"paid"have an amount greater or equal to 100?
You’re generating a yearly report for your company’s revenue sources. Calculate the percentage of total revenue made to date during the first and last years recorded in the table. Round the percentages to two decimal places.
You’re given a table called
annual_payments for an annually billed B2B SAAS subscription product. Users pay for three different products:
'Cloud Storage', and
'Mobile CRM'. How would you formulate a query to calculate the average annual retention (for each subsequent year) at the end of the year?
You’re given two lists:
Write a function
fund_return to calculate the total profit gained from investing in the index from the start to the end date. You may only purchase and sell discrete shares of the index fund. For example, if you have 23 dollars and the price of the index is 5 dollars, you can only purchase four shares.
Assume that the revenue (or loss) from the index fund is applied to the deposited funds at the beginning of every day based on the percentage increase in the price of the index and that the purchases (or withdrawals) are made before the end of each day.
You’re given a list of sorted integers in which more than 50% of the list is comprised of the same repeating integer. Write a function to return the median value of the list in O(1) computational time and space.
Given an integer array
arr, write a function
decreasing_values to return an array of integers so that the subsequent integers in the array get filtered out if they are less than an integer in a later index of the array.
Let’s say that you’re drawing N cards (without replacement) from a standard 52-card poker deck. Each card is unique and part of 4 different suits and 13 different ranks.
Compute the probability that you will get a pair (two cards of the same rank) from a hand of N cards.
What are they used for? When should we use one over the other?
What is the difference between them? What makes them useful for logistic regression?
To maximize your earning potential as a data scientist, check out this list of top hedge fund companies:
Remember that compensation is just one factor to consider when choosing a company to work for. Other factors such as company culture, work-life balance, learning opportunities, and growth potential are equally important.