Chat with an Industry Professional 2

Recently I had the opportunity to converse with Keith Finnerty from Deutsche Bank. He is an Irish data scientist and he is really passionate about his job. He actually started as an electronic engineer, working with circuit design and combinations hardware, but then he later switched to a data scientist. He also has a PhD in communications hardware. I thought it was fascinating how he completely changed careers because of how interesting he thought data science was. He also mentioned how the whole basis of data science is problem solving and model building. Our conversation went very smoothly, and he answered all my questions in an informative way. Keith described his experiences with using data science in a medical environment versus a financial environment. In the medical field, data is collected live, through experiments. It is also more difficult to put into a readable form because it is hard to capture, and there are more inconsistencies. However, at the end of the day it is rewarding because you can save lives, and you get to travel to many different clinics. Even though data scientists aren’t the front end of hospitals, they are still extremely important in order to determine the issue and help with cure development. In the medical field, there is so much experimental and patient data to go around, and it is the job of a data scientist to organize it, identify errors in testing, and display it in a comprehensible graph or chart. With this information, doctors and geneticists can diagnose a condition, or they can pass it on to researchers if it is a new disease that no one knows about or how to treat it. As can be seen, there is a lot of interconnectivity between data science and fields you wouldn’t expect to use it in. After working in clinics for a while, Finnerty spoke about his current job as a data scientist in the finance department at a German bank. He liked the change because there was a broader range of problems and technologies. You also get to work on big data and stock market data. In the finance sector, there is a step by step data science process that Finnerty uses. First you get the data and put it into a database. Then, you run analytics and build trust with the stakeholders. It is important to create a good relationship with these people to ease the business side of things.