For the purpose of this report, we have split the market between buy-side and sell-side firms. At Selby Jennings, we pride ourselves on market expertise across niche verticals encompassing quantitative finance. We are in constant pursuit of excellence delivering consistently to the clients that we serve, whether they are start-up hedge funds or multi-national banks.
Developments in the Quantitative Finance Field
Following 8 years of an administration that placed a number of regulations across the financial industry, it comes as no surprise that the most important development over this past fiscal year was Donald Trump’s victory in the 2016 election. President Trump has vouched for the repeal and/or scale back of numerous banking regulations, which has directly resulted in increased optimism in the market that has not been seen for quite some time.
Increasing diversity continues to remain an initiative for the majority of our clients. Many HR team members and hiring managers commence searches with this in mind with specific emphasis on strong female, racially diverse and veteran candidates. This initiative continues to be promoted across business lines, more work needs to be done to meet the diversity and inclusion goals of many financial services organizations.
Recruitment Growth & Decline
With increased market optimism, quantitative teams are preparing for a growing emphasis on model development moving forward. In order to execute, many of our clients are seeking to build their teams by adding Associates and Junior VP’s with 1-4 years of Front Office Model Development experience. Unfortunately, due to the Obama-Era regulations that have impacted financial markets in recent years, the demand for PhD level STEM candidates to transition into true Front Office Desk Quant positions has dropped tremendously. Over the past four years, much of this talent has gone on to join roles within Risk Management and Technology departments.
This has been difficult for most of our newly optimistic clients who have more traditional preferences in the types of candidates they are looking for. The market has most recently been developing qualified junior Ph.D. quants into strong risk analysts, model validators and quant developers, rather than the front office desk quants many companies prefer. As a result, we advise our clients to broaden the years of experience they are looking for; as well as advising companies to be more flexible in terms of the skillsets required for an opportunity. It is unlikely they will be able to find someone that checks all the boxes (derivative pricing knowledge, programming skills, regulatory knowledge, verbal/written communication skills) within that range of experience. However, companies will be able to secure strong candidates if they look for those with most of the required experience, and the personality to develop the missing skillsets.
Due to the development and technological innovation in high frequency trading infrastructure, the barrier to entry lowered, resulting in a rapid increase in the number of players in the space, over the past 10 years. But recently, rising transaction costs and failure to develop new Hedge Fund (HF) models has decreased the profits of some Proprietary Trading Firms. This has lead to some firms closing down their HF arms and attempting to reinvent their organizations. Multiple Proprietary Trading Firms have begun looking toward medium to longer term strategies focused on global equities. Some of these organizations have shifted into building equity statistical arbitrage teams focusing on intraday or week long holding period strategies. This space is not reliant on high-end infrastructure with lower transaction costs as strategies are not being executed thousands of times a day.
Asset Managers and traditionally long-term oriented hedge funds have begun exploring event driven strategies. With the election of Trump, the unexpected outcome of Brexit, and the uncertainty that lies ahead, many believe there are returns to be captured. There has been an increase in the demand for Quantitative Researchers conducting sentiment analysis and building strategies capable of capturing alpha from events. In correlation, sentiment and event oriented strategies are reliant upon analyzing large unstructured data including text, audio, videos, and other sources that are hard to numerically model using traditional quantitative methods. This is a space many quants struggle to take advantage of due to their lack of applicable skills. Unstructured data is a huge territory that requires determining the most applicable data sources, identifying the most pertinent information and subsequently extracting it. This requires a broad skillset most commonly learned by Machine Learning researchers working in Technology firms.
Machine Learning is a broad and encompassing field with, many sub-areas that have become a key area of growth on the buy side. Many organizations are looking to add experienced Research Scientists from Google, Facebook, Amazon, Microsoft, and other tech behemoths who utilize deep learning, natural language processing, data mining, and other techniques on a daily basis for innovation. For example, a research scientist may use NLP techniques to create an algorithm capable of parsing through large text data, analyzing particular sentences’ intended meaning, and outputting a result that can subsequently be used by experienced quants to develop numerical models. This can further be applied to live data as it comes in, such as breaking news or statement releases, and can be used to develop live event driven strategies.
A number of large Hedge Funds already employ Machine Learning (ML) for deep numerical data mining, but many are taking the plunge into using ML for broader purposes such as the ones described above. However, they face the challenge of enticing and attracting talent from tech firms back to the financial services sector. Most Research Scientists have vast engineering teams who provide the tools, infrastructure, and virtually unlimited resources, not only for research purposes but also for the comfort of their employees. The best Machine Learning scientists spent 5+ years pursuing a PhD in computer science for the sole purpose of landing a job at a tech giant. They are treated to a great work life balance and have a plethora of perks available.
For recruiters, this poses a significant barrier as there is a certain stigma around finance some firms; in particular Hedge Funds and Asset Managers who have a reputation for being aggressive. Compensation is not often a significant motivator, as many are stimulated by the ability to work on impactful projects that lead to innovation in the community, with many also teaching in academia, attending conferences, and releasing academic papers. This can often be a conflict of interest for many Proprietary Trading Firms and Hedge Funds that closely guard their innovations and models to prevent competitors from utilizing them. This all leads to a limited pool of candidates for these firms to hire. To get the best researchers, firms will have to engage specialist recruitment agencies to allocate time and resources to educate and attract these Machine Learning Researchers.
Technical Skills in High Demand
The rise in the use of machine learning in quantitative finance has led to an increased demand for candidates with PhDs in Computer Science; from the best programs such as University of California at Berkeley, Carnegie Mellon, Stanford, MIT, and others. However, not only is a PhD in Computer Science required, but furthermore, candidates must have a specialization in natural language processing, deep learning, deep neural networks, artificial intelligence, or data mining. Experience with machine learning tools such as Google’s Tensorflow, and the University of Montreal’s Theanos, is a must. Python remains the hottest programming language for data management and a core foundation for much of machine learning. Its syntax and development capabilities make it a user friendly language to program in for multiple tasks including lower level programming as well as research and analytics.
Note-Worthy Impacts that ‘Quants’ Made on the Buy Side
Across the buy side, the Data Science trend and the success of quant funds is convincing many more fundamental firms to explore Machine Learning. While Hedge Funds and Proprietary Trading Firms still look for top talent from academia and technology, many are noticing increased competition from long-only Asset Managers and more traditional investment firms who are now hiring in the space. The success of the quant funds is proving to the rest of the industry that valuable insight can be gained from AI and Machine Learning – even if they are just one input of the investment process rather than the force behind it.
The popularity of these candidates has also greatly impacted the hiring market. With more options and more competition available to them, leading Machine Learning talent has become more discerning and more expensive, creating a candidate driven market.
Recruitment Trends 2017
Sell Side Trends
On the sell-side there have not been notable changes in terms of compensation packages. Most desk heads are still unable to provide bonus guarantees due to the volatility of being tied to the PnL of their respective desks. As a result, the majority of banks are only able to provide verbal target bonuses which serve as a benchmark for new quants joining the team.
One of the most important points around bonuses that we have seen is, that quant teams continue to be moved into risk divisions across the street. Some banks (even top tier) are now unable to provide verbal target bonuses at all.
Beyond base and bonus package details, companies continue to be able to provide buyouts of stock, bonuses, green card transfer buyback, tuition reimbursement etc. However, in order for these to be considered and included in the final package, documentation must be provided to the new businesses HR team.
Buy Side Trends
Many buy side firms are undergoing organizational transitions to better position themselves in the market, so too are compensation structures changing. PhD graduates have typically received anywhere between $110,000 to $130,000 base salaries with minimal sign on bonuses. The industry standard salary for recent graduates had created a culture and environment where junior quants were leaving their entry level roles after merely 1 to 2 years for higher compensation in similar job functions. In the last 6 months, the best firms have been overpaying PhD graduates in the hopes of not only retaining talent long term but acquiring the best PhD talent. Amongst top tier and highly sought after organizations, base salaries have skyrocketed inching as high as $175,000 for top achievers with sign on bonuses up to $50,000.
Another reason for the increase in base salaries centers on PhD graduate mentalities. Many seek or even need the guaranteed cash flow upon graduation to pay off student loans rather than hedge their skillset in the hopes of receiving larger year end bonuses. This creates internal turmoil for hiring managers as it creates an imbalance amongst experienced quants whose salaries are on the same level of incoming new hire graduates. It is an issue managers have yet to address but will certainly lead to higher salaries across the board.
Geography Trends for Quant Teams
Unlike firms in the bulge bracket, or even the most competitive hedge funds and private equity shops, quantitative analytics and its visionaries have been known to lead their chosen people to off-beat promised lands with a few big names headquartered in Seattle, WA, Minneapolis, MN, Westport, CT, Sydney (AUS), Denver, CO and Raleigh, NC.
This does not necessarily indicate a marked trend regarding movement away from traditional financial hubs. However, it is a special characteristic of the quantitative sphere that affords them the freedom to situate themselves in financial enclaves that not only offer their teams a respite from the hectic bustle of New York, or the tiresome commute associated with living in Los Angeles, but also takes advantage of lower living costs and income tax levels. Even the top investment banks are following suit; Morgan Stanley has placed their Technology & Engineering Centre in Montreal, Bank of America spread their Quantitative Middle Office group out to Seattle and Chicago, and Deutsche Bank sent their top researchers and analysts to Jacksonville, FL. This is in line with the nature of the work, as global connectivity has enabled the technology-driven world of quantitative analytics to attract top local and international talent to environments friendly to their personalities (both introverts and extroverts have their pull-factors) and conducive to their analytical and creative responsibilities.
Even so, many firms have actively chosen to remain close to the epicenters, either for ease of communication and morale, or to simplify the recruiting process and avoid relocation costs for their target savants.
Most Sought After Requirements in the Quantitative Finance Field
In front office quantitative analytics, clients are looking for academically sound candidates. PhD’s and MFEs from top tier institutions are most desired in the market, which is elicited through the uptick in on-campus recruiting.
Knowledge of concepts like stochastic calculus, monte-carlo simulation, understanding Brownian motions and option pricing have been much more relevant while vetting candidates, even across senior level hiring. Experience is certainly important, however, an employer wants to know that the hired candidate has a strong foundation so they can not only tackle the task at a satisfactory level, but contribute much more.
Programming is a skill that is highly sought after in the front office, in particular knowledge of C++ and Python. Interview processes now encompass a coding evaluation, whether it’s on a white board or writing live code on the employers interface.
A candidate that has the above skills coupled with experience will show to be of more value to a client. It will cost less to help train/develop/catch up a new hire on the team, the individual can hit the ground running from day one and convey that they can handle the pressure.
Advice for Mid to Senior Level Quants Candidates
Given the economic optimism, many financial institutions are looking for new ways to diversify and continue to expand their business. Quantitative individuals with a strong aptitude to program and build up new systems are seeing an open landscape to interview and explore new opportunities. At a more senior level, making a transition is not as seamless. Taking time to prepare yourself as you enter the job market will greatly increase your chances of landing that next role.
As you begin to consider new opportunities, it is important to consider a number of things to make the most out of your interview process. Thinking about necessary resources, technological framework, capital requirements, co-location, API, and data sources will increase your chances at landing an onsite interview. Most of the first interviews are to discuss how transferable your experience, strategies and skill sets are.
For mid to senior level quants that are looking to move into a sell-side institution or a more junior level research position on the buy side, consider looking at added resources to the quant team, growth prospects of current product offerings, and overall plans to expand to align your interests with those of the organization so that you’re confident that your making the right move. In addition, always be open and honest about your current compensation and expectations to make a move.
All along the way, we’re here to help. We encourage anyone who might be interested in a quantitative finance role to get in touch with one of our market experts for a confidential chat about how we can work with you to take your career to the next level!