From Wall Street to Facebook - The Transferrable Skills for Making the Move From Finance to Tech
There is a common misconception that people who are working in the financial services sector might not have the appropriate skills to land a job at a fast-moving tech company.
To explore this topic, a group of Facebook engineers went live from Facebook NYC to share their personal experiences making the transition from finance to tech, in particular, joining Facebook. The conversation demystified the perceived hurdles that keep talented engineers from exploring opportunities outside of Wall Street. We caught up with them to learn more.
If you are interested in watching the full segment, the video from the Facebook Live event is further down below.
How well do skills in the finance industry translate to working at Facebook?
For engineers working in the financial services industry, there's often a misconception that their skills won't translate to a company like Facebook. However, there are many skills and technologies that overlap, including:
- The importance of distributed systems;
- The use of machine learning;
- The need to design scalable and resilient systems, leading to reliable code;
- And building for live experiences, like high-frequency trading and Facebook Live and live responses.
Can you share some of the similarities in terms of the projects and infrastructure related initiatives between financial services and Facebook?
Distributive systems in banking translates well to distributive systems at Facebook. There’s the same emphasis on stability and need to scale and react quickly to large-scale events. The need for resilient and reliable code is another critical element that runs in parallel. Related to event planning, financial services and Facebook both deal with unpredictable and challenging live environments. For example, financial services have several known events like the Russell Rebalance and Black Friday that are critical, especially for retail banks. Facebook also builds for live experiences around key, global events – for example, New Year’s Eve and the World Cup.
Are there specific projects that people generally work on at Facebook when coming out of the finance industry?
Facebook is focused on designing roles, teams and an organization that helps people do work they’re naturally great at and love doing. People perform better if they’re doing work that fits their strengths, and we spend time working with people to shape their experience at the intersection of what people love, what they’re great at, and what Facebook needs.
That opens up unlimited opportunities to have an incredible impact working for Facebook. Our mission is to give people the power to build community and bring the world closer together. It's very rewarding working on projects that ultimately help people stay connected with friends and family, to discover what’s going on in the world, and to share and express what matters to them. With that said, the scale of financial services provides a glimpse into Facebook's scale which is unprecedented; it is rare to get experience at Facebook scale. People can work on problems at Facebook that they can’t work on anywhere else.
How is working on high-frequency trading system similar to working on Facebook Live or Facebook’s news feed?
There's a great emphasis on low-latency when working with either high-frequency trading systems or across Facebook's apps and services. Both provide the challenge of developing a network that is optimized to process a very high volume of data with minimal delay. For a high-frequency trading system, you need low latency to be the first market maker to match the order. At Facebook, achieving low latency on a user's news feed means they can see their friends' post as soon as they open the Facebook app.
Let’s talk machine learning - what are the differences between financial services and Facebook?
There are several similarities between financial services and Facebook's use of machine learning technologies and techniques. These include collecting and cleaning data, generating automated features, building training pipelines and training models. The main difference is that in the financial arena, machine learning models are trained to predict the price of stock or options, but at Facebook, we use models for ranking, prediction or recommendations to create personalized experiences on our apps and services.