The Blagger’s Guide to Recruiting Quantitative Analysts
No one can know everything there is to know about their recruitment area. With so many companies, acronyms, and bits of legislation to piece together, how do you find the time to actually recruit? That’s where we come in: regardless of how long you’ve been recruiting, we’ll guide you through the complexities quickly to have you sounding like an expert in no time.
Today, we’re going to look at Quantitative Analysts and how they fit into the financial services industry. We’ll be giving you info into how they function, who hires them, and the specific lingo you may run into.
What They Do
In the simplest terms possible, Quantitative Analysts use mathematical and statistical methods to find patterns in data, which helps financial services firms make predictions and decisions. Mix speculation with concrete mathematical methods, back it up with data, and you have a Quantitative Analyst.
Quants (if you’re cool) aren’t specific to any particular area of financial services: they have their fingers in a lot of pies, and the pies just keep getting bigger. The day-to-day responsibilities of a Quant will differ depending on where they are employed. They can also specialise in specific areas, like algorithmic trading or derivative pricing.
How They Do It
Quants take large (and I mean huge) sets of historical data about a specific thing and filter them through analytical software and models. By doing this, Quants can tell how that thing has performed in the past, how it is currently performing, and can then predict how it may perform in the future. Companies use the information Quants produce to inform their decisions or build new strategies.
Companies like investment banks and hedge funds use Quants for trading. You can find out more on how hedge funds use Quants in our Blagger’s Guide to Quant Hedge Fund Recruiting. Quants at these companies will work on the front lines with trading teams to design models for pricing and algorithms for automatic trading, which they will constantly monitor and improve to reduce costs and increase profits.
To make trading decisions easily and quickly, Quants can use algorithms. At its simplest, an algorithm is a set of rules designed for a computer to follow. Quants can take sample data and “train” a computer to follow a certain set of rules on how it processes that data. For example, if the rules say to sell stocks when the data shows their value going over a certain amount, then the computer will do it automatically.
Other financial service companies use Quants as well. At these companies, Quants are more likely to be concerned with managing risk than trading. If a company is looking to expand by acquiring another company, for example, a Quant can look at that other company’s financial and operational data and determine whether the acquisition is a good bet for their company.
The Perfect Candidate
Quants can be highly specialised creatures, but one thing they will definitely need is a background in Data Science, Mathematics, and/or another maths-based science subject. Your candidate will need to be able to understand calculus, linear algebra and differential equations, and probability and statistics. Depending on the level of the role, a Bachelor’s degree may not be enough, with some companies preferring their Quants to have Master’s degrees or even PhDs.
When it comes to the data itself, Quants will need experience of using a data analysis software system, like MATLAB or SAS. A job spec may not specify which system a company uses, so this would be a good subject to quiz your prospective client on. Some companies may also want their Quants to have experience with Computer Science, or at least a programming language, if the role involves maintaining the database or coding in trading apps.
There aren’t really any Quantitative Analyst-specific certifications that you need to look out for, besides academic specialisms. The jury’s still out as to whether a Chartered Financial Analyst (CFA) charter from the CFA Institute will give your candidate the edge, as it focuses more generally on investment analysis and portfolio management. It would be rare to see a CFA charter on a job spec, but if your candidate is looking for a career in investments it certainly won’t hurt their chances.
As we mentioned above, Quants can use algorithms to create rigid sets of rules for computers to follow. To design these algorithms, Quants will need experience in machine learning. Machine learning is a kind of artificial intelligence, where a computer system is given data and trained to make decisions or predictions without direct input from a human. If the job spec you’re looking at contains the word “algorithm,” you’ll need to find someone with experience in AI generally or machine learning specifically.
The Market Trends
Data specific to how the recruitment market for Quantitative Analysts will change over the next few years is essentially non-existent. The US Bureau of Labor Statistics foresees general financial analyst roles growing by 6% between 2018 and 2028. As of last year, the UK is also showing an upward trend since 2001.
Though this is a general statistic covering a wide-range of analyst roles, as more financial services companies look to reduce their risk and improve their profits, while elbowing out competition, the demand for Quants in all specialisms will continue going up.
Even though the demand is growing, it doesn’t mean that companies are able to retain their Quant workforce once they have them. Since quantitative analysis is such a new trend on the financial services scene, Quants are finding that the more traditional financial institutions are not equipping them with the proper tools to do their jobs.
Quants have spoken of not being given the newest data management systems or of being given large projects which they aren’t able to complete. Some Quants could feel underappreciated and bored since they aren’t able to work on interesting projects. Predictably, there’s a lot of talent flow in this sector because of this, which is both a boon and an obstacle for recruiters. Your talent pool will never be empty, but clients may not be happy if all your placements leave too quickly.
Here we’ll be listing some everyday terms and phrases you may run into when recruiting Quants, so you’ll never be caught out by the jargon.
|Quantitative Analyst||Someone who uses quantitative methods to analyse data for financial services companies.|
|Big Data||Really, really big sets of data. Does what it says on the tin.|
|Data model(l)ing||Organising the data by creating models for the data to be stored in a database. Data, data, data.|
|Data validation/model validation||Checking the accuracy, quality, and source of data before it is processed. Essentially making sure that the data about to be used will not cause any trouble.|
|Machine learning||The study of algorithms and statistical models that computers use to perform tasks without human input.|
|Qualitative research||Research based on non-numerical data. So… words.|
|Data science||Using scientific methods and processes to gain insights from data.|
|Securities||A certificate or other financial instrument that has monetary value and can be traded. Broadly fall into three categories:
|Derivatives||A product which derives (hence the name) its value from something else. The value of a derivative will change based on the value of the thing its value is based on.|
|Pricing/derivatives pricing||Working out the price of a derivative based upon its underlying value, i.e. the value of the thing it derives its value from. Used in trading.|
And that concludes our Blagger’s Guide on what you’ll need to know to recruit Quantitative Analysts. Looking for leads on quant hedge funds that are recruiting? Try searching in your area and selecting ‘Quantitative Analytics’ as a function. Don’t forget to set up a saved search on Talent Ticker if quantitative analytics is one of your recruiting focuses, so you can jump on those leads as soon as they’re live.
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