Tyyppiarvon Data Science -päivä -haastattelusarja julkaistaan poikkeuksellisesti englanniksi, sillä itse tapahtuma on englanninkielinen.
”Data Science Day Interviews” -series introduces the main partners of Data Science Day. The first interview was made with Marko Sysi-Aho, who works as Manager of Digital Innovation and Health and Public Services at Accenture.
Tell briefly about yourself and how you ended up working for Accenture.
My name is Marko Sysi-Aho and I graduated already in the spring of 2002 as a M.Sc. from the department of Engineering Physics and Mathematics from the former Helsinki University of Technology. Originally I was interested in investment and did both my Master’s and later on also PhD on topics related to the characteristic features of stock market return distributions, and agent-based models generating some of those. At the very end of my doctoral thesis in late 2004 I got extremely interested in the medical and biological applications of computational methods and ended up working in R&D on that field for about 10 years. Within those years, I made two visits to finance, once for data science tasks on insurance and another time for investment risk management, but got both times attracted back to medical and healthcare field for its meaningfulness. The biggest push away from the field were limited opportunities to grow more into business. I got recruited into Accenture from a startup focusing in medical diagnostics as my ex-colleague from that place recommended me to the Digital Analytics department.
Why do you work in Accenture and what is the role of Data Science in your company?
Work at Accenture is driven by client needs in various industries. At Health and Public Services our clients take care of tasks that are important for all of us. It gives motivation to work in the field as our work directly aims at increasing client value, which at the same time serves the common good. In our projects data scientists are often “special troops” that work alone or in small groups to develop innovative solutions for gaining insight from various data. Often they also serve as advisors for a client to improve data utilization.
Which tasks belong to your job description? Which statistical methods and programs do you use in your job?
General solution architecture to help clients solve their business problems using data. Tasks and duties are really free, main idea is to bring in value for the client. As for the methods I have been using a wide spectrum of linear and non-linear methods in supervised and unsupervised settings. For example, various regression, classification and clustering methods, including random forests, support vector machines, extreme gradient boosting, elasticnet and then typically cross-validation variants for model generalization ability assessment. Personally I use mostly R.
Name three skills that you consider most important in your job.
- General Problem solving,
- ability to identify problems and map them into data, or data into them, and
- wide and deep enough background on methodologies. In general it is important to get a gut feeling of alternative solution approaches, which then can be deepened with specialized experts.
Are there any positions for students on your department and what kind of assignments students and new employees typically get to do?
Quite often people enter Accenture after graduating. There are also summer trainees. There are not really typical assignments that are given to newcomers. The assignments heavily depend on the person’s profile. On average, perhaps, there is a tendency for decently sized projects which have adequate teams.
Which hints would you give to a young statistician who is aiming to work on your field?
If you have passion for something, go for it! If you don’t, try to find a best proxy for it. If you can’t, get hooked at least by something you do! Being a good data scientist all your career is mentally extremely demanding, it is good to build also other skills along the way.