For users of the Okko online cinema, the question ‘what to watch’ is never a question. Choice recommendations are just perfect, complemented by a search delivering accurate results. Coordinating Okko search is Shuhrat Khalilbekov, Senior Data Scientist and graduate of HSE. In his interview with Success Builder, Shuhrat shares his experience of choosing a master’s degree, juggling studies with 16 hours of work a day, and serving as Senior Data Scientist at top firms.
What prompted your decision to leave Saint-Petersburg for Moscow?
I didn’t feel I was prepared to enter the labor market with my bachelor’s degree, so I decided to do a master’s in Moscow. When choosing among programmes, I was looking at whether they offered the right balance between hands-on courses and academic research opportunities. My plan was to start a career in academia and pursue science.
There was an opportunity for me to study abroad, but I somehow failed to meet my application deadline. I ended up choosing between NES and ICEF. While NES’s Master of Applied Economics looked a perfect fit for those wishing to follow PhD careers, ICEF’s Master of Financial Economics turned out that perfect ideal balance. With links to practicing experts, array of hands-on courses and opportunities for PhD research, ICEF looked a perfect choice.
Those wishing to continue their education in Russia may find it hard to find a place better than HSE
HSE is best from the perspectives of curriculum, access to industry, and faculty staff. It even helps its graduates get employed. After my thesis defense I got a call from one of the teachers – he was a member of my thesis committee – offering me a job in his startup. Even though conditions sounded lucrative, it was an entry-level job and I had to turn it down because I had by that time gained two years of experience as data analyst. It was a case of the professor coming across a student who he thought might be a good candidate for his project.
What makes Data Science an interesting field for you?
I developed my interest in data science while doing my bachelor’s – precisely in my second year, when I chose to minor in data science after having been expertly presented Dota 2 Game and how relationships between items sale and sales monetization worked. It got me interested. We used R, not Python, as our programming language, and the quality was far for being top level as the hype around Data Science was just starting. I wasn’t able to attend full course because I chose to go to Sweden on an exchange. By the time I got back, I knew I wanted to be in Data Science. I didn’t feel I had enough knowledge, though.
Before Data Science, I saw myself working in consulting or IB, but neither of these fields of economic knowledge offered enough room for technical capabilities. At that time, the Big Three companies began to create their advanced analytics areas at the confluence of conventional consulting and Data Science. So, I focused on preparing myself for a job in technology consulting by learning what larger IT companies sought in candidates.
What opportunities does ICEF programme offer in the field of Data Science?
At that time, ICEF launched its big data course. It was its second year and it was being delivered by Fabian Slonimczyk, with seminars hosted by Stepan Zimin, senior analyst at McKinsey Advanced Analytics. Designed to complement core courses, that course had turned out sufficient and a way for me to structure my previous Data Science skills and knowledge. Even though much of the course contents was known to me from my own experience and there were skills I had learned in my workplace, that course did help me get whole picture. With my experience as data analyst, I started mentoring the less experienced students. That course is sufficient to qualify learners for internships or junior positions at IT companies. Its focus on mathematics and econometrics – the two core courses taught at ICEF and the faculty of computer science – are essential for a successful career start.
Was it difficult for you to combine work with master’s study? Is it a wise choice to try and combine both?
It is possible to combine both, although there aren’t many students who can really cope. To be able to work hard while getting just few hours of sleep is about having motivation and ambition. Be prepared for a rigorous schedule that will keep you on your toes for eighteen months of having to study for 15 to 16 hours a day. I don’t think that focusing solely on studying without access to the industry will get you far.
While hard skills such as coding will be best achieved in the workplace, the knowledge of finance and economics should be sourced from ICEF. Data scientists with background in these fields and model building skills will always be an asset in the market. Not only should they be able to solve business cases, they need to know how to estimate economic effects. In my case, juggling work with study proved a perfect combination: I was working on my tech skills in the workplace and honing my business acumen by doing my degree in finance and economics. The result you get is ‘universal soldier’ who can tackle both financial and economic issues while being an expert on model development.
What are the duties of a data scientist consultant and what is technology consulting?
Let me give you the example of Oliver Wyman’s in-house startup – a banking platform, where I started my career as a junior in a small team consisting of a product owner, another data scientist and myself (we were later joined by few more pros). Our job was to develop open data-based models for assessing legal entities’ credit risk and to sell this service to banks by subscription. All a bank needed to do to verify a Russian borrower’s credit worthiness was to go to our website and enter borrower’s Tax ID. Our models, developed using IFRS-9, would also give reasons for the borrower’s being safe or loan decisions. What we were tasked with was detailed analytics and conclusions to guide credit decision. As a result, out platform has helped banks in loan decisions with a total value of tens of billions of rubles.
The consulting part of that job consisted in first interacting with the client, and then giving figures and portfolio statistics to substantiate the bank’s potential savings, the model’s performance efficiency and why it was worth buying our software.
At EY, I was basically involved in pricing projects which were a blend of classis consulting and bits of Natural Language Processing. Consulting begins where you and your client discuss your problem-solving methodology and his expectations before translating them into a digital language (business benefits evaluation metrics), giving your project the green light and measuring its performance. As to the technology side, it is about the problem-solving tools and their performance. As practice shows, not every project needs a powerful model to benefit the recipient. When that’s the case, the role of data scientist is to come up with infrastructure of where and how a certain technical solution will be supported.
Can data scientists change from financial or consulting companies to those that are purely technical?
They surely can. It depends of their roles and when it comes to analysis positions, data specialists with background in IT finance seem to enjoy greater employability than those with backgrounds in other fields.
Strong techies can be found everywhere in Russia, but only few have structured approach and understanding of where technology and business values come together
At consulting firms and finance companies, data scientists are expected to be able to interpret values as outcomes of a particular solution and how it can be predicted. While the ability to interpret links between metrics comes with practice, that of interpreting those values is a matter of talent. But in general, specialists with this background remain highly sought after by the IT companies.
What matters here is your structured thinking – the ability that comes with experience in the workplace but is best to be obtained while you are a student because with age, as you grow older, it gets more difficult to retrain and change your thinking. With tech skills, it’s completely different: tech skills can be learned at any age and quickly, and there’s a wide array of dedicated programmes.
How and where can one learn structured thinking skills?
In the workplace and by doing dedicated courses. At HSE and ICEF, they practice case-based learning and multiple mathematical analysis courses to foster structured thinking skills. I have benefitted largely from both. One big thing about ICEF is that it teaches you to endure the highly demanding academic workload combined with multitude of practical assignments. This makes you a better learner, while when you’re an entry-level specialist at a top company with a team of real pros, many of the target qualities can be obtained through mentorship and your own observations.
At which point in your career did you choose to join Okko?
My role at Oliver Wyman was more about finance and banking. In fact, I learned everything there was to learn in my capacity. The only way further was either up or within in my usual line of duties but with increased responsibility. At EY, I dealt with metallurgy and NLP, where projects are too long. I felt like I was stuck in a rut.
Then, I started a job at Citymobil where I served as universal analyst for projects run by diverse product teams. But, unfortunately, Citymobil closed and I was forced to look for a new job. I liked my role at Citymobil. We were quite a team. Just when I started looking for options abroad, many countries started to experience larger influxes of candidates from Russia. Wage dumping started to be common and I soon abandoned the idea as no longer lucrative.
I eventually applied Okko and Avito and received job offers from both. The reason I chose Okko was because some people from my team at Citymobil had landed jobs at Okko, too.
When your move is with a team, adapting to new job environment is easier
Okko is a really new domain for me, since I never worked with streaming platforms or online cinemas. My role at Okko involves recommendations analysis and configuration, which make my job an interesting experience from the perspective of both user preferences and technical complexity. Okko uses ML mostly for advancing two of its tools – recommendations and search, the former being about personalized content and the latter about relevance of search results. I am in charge of search, and this is a new and demanding challenge for me.
What career positions does Okko have for data scientists?
The standard ones – intern, junior, middle, middle plus, senior. While those in middle positions have maximum responsibility for the decision-making, team goal setting and improving the accuracy of recommendations/search, the roles of a senior involve idea generation and implementation, whether as part of a team or on one’s own, delegation of tasks, follow-up, and performance analysis.
What do you think makes Okko an exciting place?
Its core business. It can be an inspiring experience being an Okko user and knowing that you can bring changes to the screen and that these changes will be judged by Okko’s other users. Okko boasts a high-profile team of ambitious, open-minded people with vast expertise. It cares about its employees, and there are many different perks.
How can Senior Data Scientist role evolve in the future?
Data scientists can never really afford to stay idle. While there is an unchangeable foundation that we learn as students and in the workplace, there constantly emerge new algorithms, approaches, technical capabilities, requiring you to stay updated and they receive updates. I am currently studying two courses in parallel and will soon start new ones. There’s a lot going on in Data Science, and you’ve got to have you finger on the pulse if you want to stay abreast and achieve career success. At the same time, I would like to stay that “universal Data Science soldier” who is able to work on different tasks and with diverse teams.
At the same time, I would like to continue doing projects that require dedicated knowledge and can better people’s lives and society at large. Such projects are best done as part of larger teams. Joining them takes a lot of hard work and desire to grow professionally.
