Data scientists are in demand on Wall Street. Here's what you need to know to land a job

Most industries are struggling to find data science expertise, but Wall Street especially has particularly keen to hire in this area. Data has always been a big part of the finance industry, and over the last few years, top financial services firms have ramped up their spending, investing millions of dollars to recruit and train data scientists.

The skills you need to become a data scientist at a bank or hedge fund

There are constant debates about how to become a data scientist – whether to study data science, mathematics or statistics in a university, try to teach yourself or attend a boot camp. No matter how you decide to proceed, you must learn some skills one way or another in order to get hired and have a successful career.

Most people who want to have a career in data science have a background in math. Basic algebra, statistics and calculus form the foundation for understanding this field. In addition, it’s recommended that you learn C++, Java and Python or at least have a basic understanding of those programming languages. Most data scientists suggest being fluent in Python, and if you've already mastered those languages, then learn R, PHP, C or JavaScript.

Data scientists typically have a certain specialty and tend to take online courses or attend boot camps to become proficient in data mining, data munging, data analysis and machine learning. Different industries have different needs for data scientists and their respective specialties. Those who know they want to work on Wall Street can narrow their studies a bit; however, the finance industry seems to be recruiting pretty much anyone who specializes in data science.

How Wall Street deploys data scientists

Data science teams are relatively new at most Wall Street firms, which means whoever wants to work as a data scientist in a financial company must be a leader. A data scientist in finance is still a new, yet highly sought-after position, so someone that understands how to harness big data in a financial setting and with the ability to hire and manage a team is a dream candidate for these big firms.

Banks and financial institutions have the cash flow to hire a data science team, but many lack the knowledge and understanding of what exactly they need. Plus, Wall Street firms are in competition with each other to create better algorithms to make trades and build out highly functioning technology, making the desire to find a strong team even more immediate. That means the right candidates can expect a bidding war for their services.

Information security

Security on Wall Street is a top concern, so companies are moving data scientists onto teams that solve issues such as cyber security breaches and identity theft. Data scientists can create algorithms to detect uncommon behavior and alert the necessary parties. Furthermore, data scientists are applying machine learning concepts, so these algorithms constantly learn and improve. These teams are being tasked with improving models that detect malicious behavior company-wide. From internal threats to customer security, data and machine learning is improving safety.

Furthermore, data scientists are applying machine learning concepts so that the algorithms they create constantly learn and improve. These teams are being tasked with improving models that detect malicious behavior company-wide. From internal threats to customer security, data and machine learning is improving firms' safety and reducing reputational risk.

Those interested in financial risk management or cyber security should focus on machine learning models and frameworks, such as Mahout, predictive analytics and UNIX tools.

Research and development (R&D)

R&D in finance is another area where Wall Street needs data scientists. Companies can create financial indicators through unstructured text, so they use data scientists for text mining. Scientists collect text and analyze the content and its sentiment to produce market indicators.

Data is used in pricing and risk assessment as well through machine learning algorithms that can aggregate price estimates, assign a value and understand discrepancies based on the market.

R&D is a broad category in financial firms, but those interested should study machine learning.

Data science is infiltrating Wall Street

There are many other avenues individuals can go down to grab a data science job on Wall Street. Data scientists are constantly evolving, as certain elements of the industry become automated and new techniques arise. Large Wall Street firms are hiring quickly to build robust data science teams to maintain their competitive position in the market and improve their technology infrastructure.

However, data science teams are new concepts; therefore, individuals taking on that role at a bank or hedge fund must be able to evolve and adapt to structural changes. Machine learning is a highly in-demand skill for Wall Street, so those that want to secure a place in finance can smartly invest in learning new algorithms and techniques in that field. That said, there is still a need for more basic skills as well.

Eventually, data science knowledge will become the norm among Wall Street staff, but for now, it will help to separate you from the pack during your job search.

Vivian Zhang is the founder and CTO of the NYC Data Science Academy, an adjunct professor at Stony Brook University and the co-founder of SupStat Analytics and the NYC Open Data meetup. She is a former bio-statistician and scientific programmer at the Brown University Center for Statistical Sciences.

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