2023 Q1 Market Report
Not much has changed since my last report. The market remains challenging to say the least. Tech layoffs have continued, investment banks are not hiring, and competition for available roles is significant. That said, if you’ve been laid off from Meta, it likely won’t take that long to find employment. I talk to people every week who are actively interviewing and finding good jobs. It’s a matter of broadening one’s search and tamping down expectations, at least to an extent. Significantly more talent is on the market, hence job fit is more fine-tuned, as employers hold out for a talented person who matches more than the first couple of bullet points on the job spec.
Top people continue to get paid. Despite the soft market, we’ve seen some eye-opening offers in recent months. There continues to be an arms race for talent among the top hedge funds, and hedge fund hiring remains a bright spot in this down market. Many of our clients are in growth mode, adding traders, researchers, quantitative developers and developers as quant and technology heavy systematic trading continues to grow. Many of the roles we currently see are on the investment side where standards are extremely high and requirements very specific.
Investment banks are another story, however, as most banks are frozen on hiring. Replacement hires, if possible, are likely to be done in lower cost regions. Bonuses, which were flat or down in 2022, will likely stay flat this year. While bonuses are supposed to be based on individual performance, team performance, and company performance, what’s left unsaid is that the bonus pool is largely dictated by what people are being paid outside the firm. The most important macro compensation metric is what must be paid for retention, and firms won’t be too worried about retention at the end of 2023.
Machine Learning on the Rise
Well that sure sounds stupid. But specifically, we are seeing significantly more demand for ML in trading strategies. To date we have seen relatively small, targeted ML research teams. Now many research teams are looking for ML experience in addition to the traditional math/data/research backgrounds. As for the qualifications they are seeking, it’s usually PhD’s with significant depth in the discipline. While ML is widely used at Big Tech firms, the implementation implications are different. It’s one thing if a model is off and ad imprints aren’t optimized. It’s quite another if your trading system is flawed. I expect to see growing demand for machine learning engineers at our clients.
HackerRank Skills Report – Data Engineering
HackerRank recently published their HackerRank skills report for 2023 based on their testing data (sharing my copy here). I was surprised to find that HackerRank doesn’t edit their report any better than, say CareerBuilder, given that they are a “techy” company. That snarkily said, the one thing that stands out, to nobody’s surprise, is that the world is all about data now.
Note the growth rates for Data Wrangling, Data Visualization, Data Modeling.
Here’s a tip: If you want to add skills that increase your value, focus on data related skills – and not Machine Learning. Sure, ML skills are valuable, but from a supply/demand perspective, those skills are no longer scarce. Just about every recent grad resume I see shows data science and machine learning at the center of their curriculum. What’s not common yet highly valuable, and thus well rewarded, are other crucial data skills.
Data Wrangling is an informal term that we can equate with Data Engineering. Data Engineering is massively in demand, and it’s more than writing data pipelines. Yes, data pipelines are part of it. But the key skill/experience that every company needs is a holistic understanding of the data life cycle and the ability to build systems that provide sophisticated support of data from inception/acquisition through transformation and ultimately to some number of end states. Let me know if this is your specialty – I have a high paying job for you!
Further:
“Over-emphasizing theoretical ML knowledge in assessments and interviews is a common mistake. Teams often neglect core software engineering skills when evaluating ML engineers.
As AI-based products become more mainstream, it's crucial to put equal emphasis on software development skills. They are key to building practical, scalable, and reliable ML applications that can be deployed effectively in the real world.” Ankit Arya, Principal Product Manager| Machine Learning at HackerRank.
From teamblind
Poster:
Snap engineer's coding skill blew my mind
Just interviewed a Snap L4 recently and the dude got all strong hires (incl SD round). I did 30+ onsites and phone interviews and almost nobody got to the end of my question and code it out completely.
This Snap guy went in the right direction within 2 minutes of hearing a question (a harder end of LC hard), and completed discussing the solution with me within 10 and coded it out in another 10-15 minutes... This guy is easily top 1% of all the candidates interviewing at Google. Are Snap engineers usually like this or just this person?
Reply:
If you're asking hard end of LC hard, then you're a dick.
Current Priorities
Legend:
SE: Software Engineer / QD: Quantitative Developer / QR: Quantitative Researcher / HF: Hedge Fund / IB: Investment Bank
Buy Side
Most in Demand: C++, Python, QuantDev, Data engineering, Execution, Miami, industry experience
Java/KDB/Python Engineer – uber dev for strategic technology group – HF
QR Lead – mid-sized prop trading firm
Research Engineer – HF
Research Scientist – PhD – HF
Junior QR – python - HF
Senior QR – Execution – HF
Junior QD – Python - HF
QR – alpha researcher – medium frequency futures – HF
Junior QD – C++ - HF
Cloud engineer – AWS – HF
QR – all levels – HF
QD – research engineering – Python, C++ - HF
QD – model impl – Python, C++ - HF
Mid-Senior Java Engineer – Risk/Pricing – HF
SE – Commodities – HF
QD – Commodities – HF
QR – Commodities – HF
Mid-Senior C++ SE – HF
Jr-Mid C++ SE – HF
Senior Java SE – Trade Flow – London/Miami – HF
Senior Data Engineer – python - NY/Miami/ - HF
Data Science Analyst – San Francisco – HF
Web Developer – Typescript, Python, data – San Francisco – HF
Mid-senior C++ SE – equity trading – HF
Mid-senior C++ SE – low-latency execution - equity trading – HF
QD – python, c++ - Prop startup desk
QR – python – Prop startup desk
Senior Data Engineer – Trading Desk – HF
QD “superstar” – 2-5 years – Python – Trading Desk – HF
SE C++ - Cloud – Trading Desk – HF
QR – mid-frequency – alt data experience – Trading Desk – HF
Head of BCP – HF
Jr Python SE – Austin – HF
Data Engineer – Vertica, Clickhouse – HF
Senior Java SE – equity derivatives – risk/pricing – HF
Senior low-latency Java SE - HF
Sell Side
Most in Demand: Diversity – with few exceptions, just about the only way IB’s can currently open roles in NYC if for diversity hires.
Senior UI Architect
Senior Java Engineer w/Knowledge Graph experience
FinTech
Jr-Mid Python Data Developer
Senior Scala Engineer