My Ph.D. degree is in Industrial and Systems Engineering and my area is in Multi-variate statistics of modern or manufacturing processes. After that, when I was thinking about it I thought that I needed to do something interesting like some kind of medical stuff, so I went to Arizona State University to do my postdoctoral research. I went there and started working in neuro-engineering mostly, working on holding models of the neuron systems of non-human primates and animals. After that I stayed at Arizona University for four years and I decided that I would like to go into the industry because it is more exciting. I joined a small startup company which was a financial consulting company kind of a big stock financial consulting stuff more like a financial service company, they had different clients. The field models build financial solutions for them, like a customer's problem for detection or healthcare Problems like how do you know how many days a patient is going to stay in the hospital for years that's a critical issue for the Insurance company. I had a very interesting experience there actually I did not do any customer project but my manager put me to attend these kinds of operations such as scheduling. The reason I guess is that the company is benefited from these kinds of activities because we're small companies, they need to have some kind of check records in different problems. I spent about two years and I spent most of the attending those competitions and I did pretty well in some of the competitions. That's a really good experience for me, especially for my background, I work for the Statistics area but actually it is way more advanced than that. It's just right. So that really gave me an opportunity to polish my skills to get exposed to different moments such as recommendations such as feels care such as financials, etcetera. I spent about two years doing those compositions and think that I really need to have hands in the industry probably because the difficulty, the challenges that you are not going to be encountered in machine learning and machine learning compositions, in those compositions they have screening for you, most of them and the problem redefined to and all you want to do is to try different features you nearly missed already tried different models something that exactly improves your ranking but once in several scenes in the technical industry it really means that you need to be able to understand the business problem and try to translate the problem into some rational solution, that's something used in real life, they don't always prepared to you. You have to heal them and be able to handle the different types of data gives men have, which is messy. So I decided to draw on, Leave the company on the night on the Walmart labs, that's also very interesting when we're there I was not doing machine learning It gives me the opportunity to be exposed to be in the Walmart, every minute millions of users are coming shop and on the other hand, I was able to learn on the practice. I was leading a small team to pull some kind of internal Tool business intelligence called dashboards for the well being of use. After about 1/2 year, I'm still busy in my work, my life, machine learning and reading books. Learning was a kind of high tech company, so that time I got the opportunity to work for Microsoft and I moved to Microsoft in Europe in 14 and that's where I am now.

In my current team we are working with external capital partners of Microsoft, External partners are like I give you some examples. External partners we used to work with were big enterprises such as Nestle, Starbucks and Walmart, it can be any such company. We call them customers they are called customers of us or partners that you have no kind of problems with regular business operations. They feel because they have data, they have something that they want to build some kind of cloud-based solutions. They feel such solutions can be good for the business also this solution does not only include the machine learning model but also figure out how you can deploy this model into their production system so that they can use this on a regular basis. So this is my major responsibility, it's to work with partners, to go out to design a machine learning solution and tackle the business problem. We are supporting wherever the customer is from, so you can be from Europe, Asia, South Africa or South America, We are global worldwide. Many times we need to go fly into the customer's site, walk with them to discuss and understand their business problem very clearly and come up with a machine learning solution. So roughly we travel all over the world. I travel approximately 20 to 30% of the time. Microsoft office is pretty Flexible unless you have a meeting or you need to meet with someone personally, you have the freedom to work anywhere you want. You can work from the office, work from home, Starbucks or hotel. Some could start a tail or something like that, these days you know those kinds of virtual meetings such as Microsoft teams where you can meet people use all the using different virtual channels to meet with people within Microsoft or your external partners.

These days because of the machine learning in artificial intelligence is preparing a very popular and advanced solution for solving different problems. Before deep learning R and Python are the most common tools used by scientists and these are the most popular and talked about tools too. People don't send us their issues right off away, they look into the statistical backgrounds and particularly python is a much popular tool in people with an engineering background so python is more advanced than other tools. From that perspective we were mostly using python here and talking about deep learning people work here just for the experience. New tools and software are also coming up very quickly so it also depends on the tool most times it is also because of the way in which we work with partners, we also welcome young people collaborating with our partners. For example, we have won the project we have signed from me, but we're also expecting to take a census from the other side so you really would shoot the framework of the scenes that the partner is comfortable ways or his likes. Once the department becomes more familiar with us and you have the intention to learn python is an easy tool.