I would say that my story is long and interesting one not sure if we have all the time needed for it, but I would say that most of the relevant bits begin when I started in my undergraduate college career. I come from a background where you're lucky to be able to afford to go to college, so I was fortunate enough to get the scholarships and things I needed to be able to attend. What kind of got me to where I am today is what I would say is kind of a combination of my education at the University of Louisville in addition to other resources, so they really boil down to the three things and those three things would be dedication, hard work, and leverage. I think it's important to leverage anything you have at your disposal in order to get where you need to be and to be dedicated to your own success. So that played into my story and kind of how I dealt with certain incidents that shaped my career, such as developing relationships with mentors, leveraging online learning platforms like Coursera, edX, Datacamp, and Udacity basically, you name it to get the knowledge and the skills that I needed. All of that has been a combined effort, it has been one supplemental to the other in order to get me to where I am today.

So responsibilities that I handle at work, I'm the lead Data Scientist for FordPass and Lincoln Way globally for our analytics team here and responsibilities that I handle are managing the team, prioritizing tasks, working on more in-depth analytical projects as the lead data scientist, I would be the one with the most experience and the widest skillset. The decisions that I handle at work are for prioritizing tasks and kind of interfacing with the business to get the business what they need in order to perform at their best. Sometimes that can be challenging and it can involve a lot of meetings it can involve a lot of discussions with the team and with the business to make sure that we do provide those insights and those analytics that are needed. I would say that right now, I work a little over 40 hours a week and that split between home, the office and abroad. My most recent trip was actually to Chennai, India for two weeks interface with our team there.

We use a pretty diverse set of tools, I think, ranging from programming languages to tools that are more GUI based. So there's a lot of R and a lot of Python here, which makes sense and those are the two primary open-source data science languages we have a lot of that. We also have a strong presence of tools like ClickView and Alteryx. Other tools, like DataRobot and some more tools that are specific to certain parts of the business, like Rally for project management or other things like that. As far as algorithms are concerned though we have, I think if you took a catalog of the algorithms that we use, I think at least every single one that exists would be used at least once for something. We have the familiar favorites, like some classification algorithms like, of course, XGBoost is really popular right now that we do use a lot, but I would say some of my favorites, I mean, I don't know if I have favorites, but I do prefer to do a lot of heavier duty analytics and more in-depth visualization with R and Python, as opposed to some of those other tools just because I have more freedom to do what I know needs to be done and I have more ability to leverage my skills, the GUI abstracts so much from the user that if you need to do something very specific, it may just not exist but with a programming language, you can basically do whatever is needed.