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Alexander Anikin, 2017| Align Technology, Senior Machine Learning Engineer
Meet Alexander Anikin
why skoltech?

The first reason is that I wanted to switch my current job to something requiring more scientific food for thought (before Skoltech I've worked as a business analyst at Deloitte) and at least partially relevant to all that complex mathematical basis I've learned at MSU (graduated from mechanics & maths department). I've decided that switching fields of study is the best way to go!

The second reason is I have never seen before Skoltech such a great team of lecturers and TA's having so much experience not only in teaching cutting-edge things at foreign universities but also working with real companies successfully applying their research, this was impressive and the approach of learning when you are getting fundamentals within direct applications into life seemed the most perfect for me.

Last but not least - with Skoltech I was able to leave my job and fully go through studies without attempts to catch two birds with one stone trying to work and study something else simultaneously.

To sum up — I just wanted to learn and do something exciting, something more important!

what do you do now?
After the first year of my master's degree I joined Yandex' autonomous cars project as a part of a summer internship which gave me a huge boost in skills. At the very end of the 2nd year I joined Qiwi Bank company as a Leading Data Scientist working on development of classical ML models from scratch for prediction of conversion on requests for one of their credit products, finding target auditory and recommendations of the next purchases for POS-active clients. After 6 months I left Qiwi and joined Align as a Senior Machine Learning engineer starting working mostly with analysis of dental images (classification, detection, instance segmentation tasks) in a few months I became a Team Lead having a group of 3 developers and 1 QA engineer but after successful launch of a project which lasted for more than 2 years (started before I came) I decided that at that point people management (which eventually was an essential part of my job) required too much emotional powers and now I'm on Expert ML Engineer position technically leading couple of projects and working on deep learning applications for 3D mesh data analysis and fusion of different data sources (2d with 3d).

During (and some time after) studies at Skoltech we've tried to convert our scientific study to a startup related to automation of recyclable waste sorting process but didn't succeed mostly due to the very unfriendly environment in the garbage collection business in Russia. Another attempt was when me and 2 friends of mine tried to launch a startup based on application of deep learning in the field of retail but after about 1 year of work we decided to switch to our major jobs as it was moving very slowly and outcomes were not clear at that point.
what does your typical day look like?
I have a bit shifted schedule as we're working closely with teams located in the US (which is good since I love my business trips to California and North Carolina!), so my working day starts around 11am and lasts typically till 8pm. Work/life balance is close to perfect, no significant overworking and friendly atmosphere. We're trying to follow best practices of agile having all that stuff like stand-ups, weekly sync-ups, sprints & retros etc. As a part of my responsibilities I'm technically leading 2 projects which means that apart from direct contribution (writing code) I'm responsible for generation & challenging of scope and approaches for solutions of different problems which I share with my teammates. From technical standpoint I'm not only working on native research building models and new approaches but also responsible for wrapping those models into services for launching into production, also I'm actively contributing into our internal machine learning platform developing custom libraries and tools to ease our work.

Data Science
Data scientists are going to be among the most demanded specialists in the hi-tech market. The purpose of our program is to meet this demand and to equip the most talented young scientists with high-level knowledge and experience in machine learning, deep learning, computer vision, industrial data analytics, natural language processing, mathematical modelling and other important areas of modern data science. Learn more>>
how do you apply what you learned at Skoltech?
Actually I can say that my current job is directly following up on what I've learned at Skoltech. I've started with applications of deep learning to image processing which was a primary subject for me at Skoltech and now extended knowledge to 3D data processing, so I won't be able to do anything of that without what I've learned for my master's degree. And what is more exciting for me is that now my work does have lots of research just like it was during studies. Another important aspect is leadership & soft skills which I've learned from different projects at Skoltech and now applying it almost everyday: for me it's an ability to have own independent opinion despite the authority of other people and not be afraid to propose it, ability for consistent challenging ideas and approaches to come up with the best solution, ability to ask correct questions and change mindset to something more global than just your local tasks, ability to present and defend your ideas.

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