Data Scientists in FAANG vs. Wall Street
Three things I didn't know until I visited New York
Hey friends,
I just finished my month-long trip to New York City and met 20+ data scientists. There are still a lot of data scientists working in tech companies like FAANG, but you'll also meet many DS working in finance, and the roles are quite different. Today, let's talk about the DS roles in finance vs. tech. If you are interested in data science in finance, here is a great webinar on "building a stock market advisor chatbot using OpenAI." Register here if you want to learn OpenAI APIs and FinTech use cases.
My opinion is based on anecdotes, not big data, but I think you'll get a better sense of the DS roles in tech and finance after reading this. Let's dive in!
#1 More levels; bigger titles
In the finance world, there are more levels than those in tech. A friend of mine works at American Express, and he told me there are levels from 30, 35, 40, and all the way to 90. While in tech, the level is more flat. The junior data scientist is usually level 3 or 4, and the principal is 6 or 7.
You'll meet data scientists and engineers with "VP" in their titles, which is different from the VP in a tech company, who usually manages multiple directors, and it's a senior leadership role. However, the VP could be an individual contributor (IC) in a finance firm, and it's equivalent to a senior role in a tech company.
A friend told me that the VP title for tech IC roles is because some finance-related regulations only allow employees with the VP title to work on certain projects. I haven't verified this, but let me know if you know other explanations.
#2 There are more diverse roles
Instead of "data," you see the word "quantitative" in many titles.
Quant Researcher
In the financial sector, quant researchers design and develop models and strategies to answer complex questions, predict market movements, and identify investment opportunities.
Similar to the data scientist or research scientist role in a tech company.
Quant Engineer
They implement, optimize, and maintain the algorithms and models developed by quant researchers. They bridge the gap between theoretical research and practical application by ensuring that models run efficiently and reliably in real-time trading environments.
This is generally less popular than the quant researcher role but provides much value to the team. It's more like a data science engineer or MLOps engineer role.
People use this to transition to a software engineer role if they started in data or statistics but didn't have enough development experience or the other way if an engineer wants to transition to quant researcher but didn't have much experience in data science and ML, they can learn from this role.
It seems like there are rarely principal levels for quant engineers, and the closest senior IC role for this function is a software architect.
#3 You can't stay IC forever
In tech, you can tell your manager you don't want to become a manager and want to be an IC after you reach a senior role as long as you deliver results.
But in finance, a very senior IC role is rare. Your company will encourage you to become a manager, and that's the way they grow.
And they want you to grow, get promoted, and move up the ladder. If you stay at the same level for too long, you might not survive.
A few other things
When it comes to hiring, they focus more on whether they like working with this person. Technical skills matter, but they also want to ensure you are likable and have good communication skills.
More focus on compliance if you work in finance. The risk of the model is not just losing money, but you might break laws for things you build; there are more due diligence processes and standard procedures. In tech, teams can push a new model in production after A/B testing and don't need to think much about the law.
You'll get a yearly bonus on top of your base salary, while in tech, some companies don't give bonuses, and it's base + equity.
What do you think about the differences between DS roles in tech and finance? If you have your own experiences, I'd love to hear from you. Simply reply to this email.
That's it for this week. Before you go, if you don't want to miss the webinar on AI in FinTech, secure your seat here.
Now I'm in LA writing to you. Heading back to San Francisco in a few hours.
Until next time,
Daliana
