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Data Science Vs Financial Engineering

A lot of aspiring financial professionals or even those who are in the financial industry but looking at new vistas in finance ponder if they should specialize in data science or financial engineering. Perhaps three to five years ago they could be treated as completely different skill sets. Increasingly though as data science matures, niche areas are developing. Financial data science is fast emerging as one of the fastest growing careers. Financial engineers have a skill set that are both difficult and lucrative. What’s not to want in a person who has not one but two sets of deep analytical skills? Let’s shed some light on the data science vs financial engineering debate.

Data Science vs Financial Engineering

Instead of jumping to conclusions vying for one over the other, it would be a good idea to assess both areas independently.

Financial Engineering

The Norman and Adele Barron Professor of Management at Boston University, Zvi Bodie, defined Financial Engineering as:

“the application of science-based mathematical models to decisions about saving, investing, borrowing, lending, and  managing risk”

Financial engineering is also known as computational finance or mathematical finance. Some of the organizations that employ financial engineers include regulatory agencies, commercial banks, hedge funds, insurance companies and corporate treasuries.

Financial engineering is applied to many areas in finance such as pricing derivatives instruments, financial regulations, deal execution, corporate finance, portfolio management, risk management, trading and structured finance. There is a lot of focus on assessing and managing risks in financial products.

In its true sense, a financial engineer is a specialist who works with mathematical formulas and programming tools, and applies his knowledge to areas of finance to build data-driven financial models. How a financial engineer adds value is by helping in improving the quality of financial products as well as create new financial products.

A financial engineer is also expected to work as a node between finance professionals and the tech team. They help provide development skills for analytical processes. They also help develop financial and analytical strategies for decision-making. They have to keep a watch for new and upcoming trends with regards to fiscal processes, big data and the like.

Payscale.com reports 2017 salary figures for financial engineers to be at a median of $80,000.

Financial Engineering – Job Description

For Instance Fannie Mae has advertised for Financial Engineer for Credit Risk Modeling Applications. The compensation offered is $70,000 to $130,000 Annually. The job description requires these skills.

This is a contractor to hire position at Fannie Mae to build financial modeling applications. Position will be part of an Agile team that is responsible for developing, enhancing and supporting multiple financial applications that model credit risks and credit losses. Successful candidate must have 2+ years of solid programming experience in Java and with math modeling experience. Must be a quick learner with strong analytical and problem solving skills. Must have good interpersonal and communication skills to work effectively with modelers, business analysts and business users. Ideal candidate would have computer science and programming background with training in finance.”

All this information helps us conclude that a financial engineer is expected to have in-depth knowledge of finance as well as software languages. A financial engineer is expected to harness both these diverse skill sets to arrive at strategies and solutions to assess risk and manage assets.

Data Science

Data science is an emerging field and has gained traction only in the last few years.

While there is no standard definition of what a data scientist does, the role of a data scientist involves working with data to identify meaningful patterns and insights that are otherwise hidden with the objective of helping businesses take data-driven decisions. Data scientists use data as their raw material. They clean and filter this data to help businesses make decisions. They also help them see patterns in the data which may lead to key business insights.

Neha Kothari a data scientist at Linkedin answered queries about her job in a YouTube video. She says the technology she uses for queries is Hadoop. She also uses SQL for certain data sets that are smaller. For data modeling she uses R or Excel.

The real challenge according to Neha is to get the data that is relevant and useful to answer a particular question. The question could be if users are not completing the registration process because they find it difficult. While Linkedin captures all registration data, this query needs a honing in to get the answer. She uses R more extensively. Chief data scientist at IBM analytics Jennifer Shin also feels that R is a more powerful language and is being used more with big data.

Data scientists look at actual user data to arrive at their conclusions using software tools to assess, analyse and arrive at tangible strategies that the business can use. It does not pre-suppose but extrapolates captured big data to plot the way forward. Google, Amazon, Paypal, Linkedin are some of the businesses which are willing to pay data scientists prime packages. Average salary packages are currently pegged at $120,000.

Data scientists need programming skills, data base management skills, mathematical, simulation and optimisation skills. These skills need to be blended to achieve answers to business problems. This needs to be communicated to the business leaders, so that they may use it for strategizing. A data scientist is expected to extract, transfer and load from the raw data. This requires good programming skills. R and Python are particularly useful in this regard.

Data Science in Finance

Data science, as applied to finance, is the field where you build systems and processes to extract insights from financial data in various forms. The finance professionals have always been doing data science in the form of statistical analysis, forecasting, and risk analysis, among other things, however, we now have an industry recognized term for it (data science!) and formal career options around it.

The opportunities for financial data scientists are challenging and exciting. Since industries are aware but not fully clear about how data science can help their business, it would be worthwhile to tackle existing issues in the business and how they can be effectively handled and resolved using data science.

For example Finance Train’s courses on data science for finance professionals who are seeking to migrate to the data science field aims at helping build their skills. R programming, Python, data visualization, predictive analytics are some of the skills it hopes to equip their students with.

Financial data scientists are expected to have financial engineering, financial risk analysis, programming skills (Java, R and Python), mathematical skills and statistical skills in their arsenal. With these skills in hand, no wonder you will become a formidable asset for your organization.

Data Science Vs Financial Engineering

Data science is a broad field and applies to all industries while financial engineering focuses specifically on financial issues. Someone who majors in data science can apply for a job in many broad fields such as IT services, marketing, consulting, and finance, among others. So, if we narrow the scope of data science as applied to finance, then it is possible to do a fair comparison between data science and financial engineering. While there is quite a bit of overlap between the two in terms of the skill sets required, the roles are not necessarily the same. While financial engineering focuses on creating viable financial products, assessing and managing risk and build financial models (for example, using Black-Scholes to build a new options product), data scientists is more about data-driven decision making and also makes use of machine learning to predict and automate decision making. For example, a data scientist might crunch data in a retail loan portfolio and apply it to build an algorithm that could predict the probability of a loan default.

Data science vs Financial engineering: The fact is that finance and technology are fast merging. Several lower end jobs in finance will get automated. The scope for growth for finance professionals lies with those who have equal depth in both skill sets. Make your choice wisely.

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