Engineer to Quant? Challenge Accepted

Transitioning from Software Engineer to Quant Research: a Challenging but Possible Path

Over the years, I have had many conversations with SWEs aspiring to transition into QR roles. While this career shift is not impossible, it is undeniably challenging.

Last week, I wrote a post on LinkedIn about the challenge of switching from Software Engineer (SWE) to Quant Researcher (QR) in Systematic Hedge Funds. This article aims to expand on the topic and guide those considering this career move.

This article is not to discourage but to provide a realistic perspective for those looking to make this transition so you can plan effectively.

Understanding the Challenge

The primary obstacle facing SWEs when applying for QR roles at top hedge funds lies in the stiff competition they will face. Typically, you will be up against candidates who have extensive academic training in the theoretical underpinnings of quantitative finance, namely master’s and PhDs in Mathematics, Statistics, or Machine Learning from the world’s best universities.

It is worth noting that Physics is also a popular degree in the quantitative research field. The reason is that physicists are trained to break down intricate problems, develop models, and find solutions using mathematical and computational techniques that transfer well to financial modelling.

Quantitative research requires a deep understanding of financial theories, stochastic calculus, and advanced statistical methods. While a computer science degree does cover essential maths concepts, the depth and breadth of theoretical knowledge required for quantitative research are different and more advanced.

This gap often puts SWEs at a disadvantage compared to candidates with specialised education in these areas.

Strategies for Transitioning

So, what can a SWE do to facilitate this transition? Here are some potential pathways to explore:

Consider Further Education

Pursuing a master’s degree focused on mathematics or quantitative finance can significantly boost your qualifications. This will equip you with the foundation required to stand a chance during the interview process. Typically, those interviews cover:

  • Probability (very important)
  • Mathematics (linear algebra calculus etc..)
  • Computer Science (leet code medium to hardcore style problems is required for the top tier systematic hedge funds and prop trading firms)
  • Classic ML (including ML theory i.e. optimal solution for linear regression, derive Principal Component Analysis from scratch, etc.)
  • Modern ML (especially Deep Learning most recently, but only for firms actively using DL)

While this option is time-consuming and might not be feasible for everyone, it provides the theoretical foundation necessary for quant research roles.

As an example, I have recently worked with a candidate who started his career as a Front Office Developer at a large Investment Bank before transitioning to a Quantitative Developer role on the buy side within a systematic equities desk. After a couple of years, he decided to take a year off to complete a Master’s degree in Statistics, and he has now successfully transitioned to a Quantitative Researcher role at a systematic fund.

Start as a Quant Developer

One of the best ways for SWEs to move into quant research is to first get a quantitative developer role. This position allows them to work with quantitative researchers. It gives hands-on exposure to the tools, data, and methods that underpin their work.

A quant developer role offers exposure to the diverse financial data sources that are used by QRs to build quantitative strategies. This practical experience in cleaning, processing, and extracting features from complex datasets is crucial for quant researchers.

Additionally, this role provides an opportunity to learn the nuances of different asset classes, market microstructures, and trading dynamics.

It is also not uncommon for Quantitative Developers to be given the opportunity to directly contribute to the development and optimisation of quantitative models.

Ultimately, a quantitative developer role can be a valuable stepping stone. It lets SWEs bridge the gap between software engineering and quantitative research gradually.

Internal Transition

Switching roles within your current company can be more manageable after proving your skills in a related position, preferably a Quant Developer role within a front office environment where you work closely with QRs and traders (for the reasons mentioned above).

If open to the idea, your company can gradually expose you to projects that build up your skills towards a QR role over time. Firms often prefer to promote from within, as it mitigates risks associated with an external hire’s cultural fit and ramp-up time.

Demonstrating your value and expressing your career aspirations to your employer can open opportunities for an internal move to a research role. I would advise you to proactively seek out mentorship from experienced quants within your company.

Self-Learning

For SWEs committed to making the transition to quant research roles, self-learning is indispensable. By dedicating time and effort in your spare time to building a strong foundation in machine learning, data science, and quantitative finance, you can bridge the knowledge gap and demonstrate your ability to make this transition.

Engaging in open-source quant projects or participating in competitions like Kaggle provides hands-on experience. These platforms allow you to apply your newly acquired skills in real-world scenarios, tackling complex datasets and developing models. You can then use your results to showcase your capabilities to potential employers.

Here are some reading materials I usually recommend to candidates going for interviews at top systematic firms:

Again, this might be time-consuming and would require months of reading and preparation, but if this is what you truly want, these resources will steer you in the right direction.

Consider Algo Execution Research

While the path to becoming a quantitative researcher can be challenging for SWEs, an alternative avenue lies in the field of algorithmic execution research.

This specialised domain focuses on the technical intricacies of market structures, order matching mechanisms, and the impact of diverse trading venues – all with the goal of minimising trading costs and maximising execution efficiency.

These positions are particularly prominent within High-Frequency Trading (HFT) and Proprietary Trading firms. Algo Execution Researchers develop and optimise algorithms that execute trades efficiently.

For SWEs, this field presents a great opportunity to capitalise on their strong programming skills and experience in algorithm development and optimisation. This field is particularly well suited for SWEs with expertise in low-latency systems and high-performance computing.

Additional Tips

  • Network with industry professionals: Connect with professionals in the quant finance industry to gain insights and advice. Networking can also lead to mentorship opportunities, which can be invaluable during your transition.
  • Stay updated on industry trends: The field of quant finance is constantly evolving. Stay informed about the latest trends, technologies, and methodologies to ensure your skills remain relevant.

Conclusion

Transitioning from a software engineer to a quant researcher in a systematic hedge fund is challenging but achievable with the right approach. By pursuing further education or through targeted self-learning, and gaining relevant industry experience, SWEs can bridge the gap and position themselves for a potential move into quant research roles.

Written by Alex Jouatte

Crypto Corner

Is Bitcoin on the cusp of a parabolic breakout?

The market cap for all BRC-20 tokens now stands at more than $2 trillion, an increase of more than 250,000% in less than a year. Meanwhile, growth in inscriptions — the process by which unique assets such as Ordinals are created on the Bitcoin blockchain — has been equally meteoric.

More than 66 million inscriptions have been recorded on the Bitcoin chain to date with more than 6,8 BTC spent in fees in the process — that’s over $466 million. The total number of inscriptions has doubled since October. It’s easy to spot analogies between Bitcoin assets and the ICO craze that saw Ethereum’s multi-token era kick off in earnest in 2017.

ETH experienced a tenfold increase in price from early 2020 (below $200) to mid-2021 ($2,000 and beyond) thanks to the burgeoning DeFi sector’s development. Subsequently, a positive feedback loop emerged: the growth of ETH made DeFi more attractive, thereby stimulating demand for this cryptocurrency. Judging by the TVL chart data, Bitcoin is now where Ethereum began its active growth in 2020. As decentralised finance increasingly adopts BRC-20 tokens, which can start to displace competitors, Bitcoin will react to these shifts positively due to increased demand.

Despite Bitcoin’s higher capitalisation, it may not experience growth as meteoric as ETH did in 2020, given the absence of a low base effect and stricter regulatory conditions. However, even a weak surge could still produce a big change in value.

Bitcoin developers would do well to copy Ethereum’s homework by heeding the innovations that served as catalysts for that blockchain over most of the last decade.

Written by Valeriu Veriga

Market Headlines

Weekly inflows into crypto funds totalled $185 million for the last week in May, with monthly investment fund inflows reaching $2 billion, bringing the year-to-date inflow of capital to more than $15 billion – according to the latest CoinShares “Digital Asset Fund Flows” report, released June 3.

Segantii Capital Management Ltd. gave investors its first performance update after authorities in Hong Kong charged the Asian hedge fund with insider trading, according to people familiar with the matter. The $4.8 billion Segantii Asia-Pacific Equity Multi-Strategy Fund was up about 0.3% in April, said the people, citing an initial estimate provided by the firm. The fund returned 2.51% in the first quarter, according to a previous update sent to investors.

The intersection of Web3 and artificial intelligence (AI) is rapidly gaining traction – industry experts are addressing the centralisation issues in AI development by using blockchain technology to democratise access to AI resources, ensuring fair compensation for contributors, and allowing secure usage of proprietary data, aiming to reshape the AI space toward a more equitable future.

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