Led Analytics Efforts for Gardenscapes (One of TOP-10 grossing mobile games according to various sources)- Built one of the strongest DA/DS teams in the company. Hired 4 highly qualified professionals and helped with their growth into senior/lead roles- Introduced planning and processes that made Analytics 1) Verifiable 2) Scalable 2) Allowed to deliver results aligned with business KPIs on time. As a result we've increased our team efficiency by preemptively identifying most important projects to work on and transparency among the team of 10+ stakeholders. This approach was universally praised and sometimes copied by other analytical teams.- Identified and delivered results on various growth opportunities such as 1) 2-5% increase in user retention for specified groups of users 2) revenue growth by 1-3% in specified markets due to adjustments in local pricing- Introduced usage of ML-models for 1) forecasting the effect of live-ops events on revenue that helped us identify under/over-performing activities 2) user segmentation for the core game and various live-ops events- Developed and introduced various KPI alert and monitoring systems to ensure that we catch problems ASAP. The systems were constantly used by management to monitor state of the game.- Introduces usage of AI tools in our team to increase productivity which resulted in 15-20% faster task completion for complex problems.- Successfully co-led (with DE) several infrastructure initiatives: 1) switch to a new tracking system (project planning, tracking principles & schema) 2) introduction of faster data tables structure to ensure faster data access and correctness (table structure & work principles)

I was responsible for researching applicability of ML-algorithms and Neural Networks models usage in prediction of changes in rivers water regime types in the changing climate.We're able to prove that it's indeed possible and published the following article https://ges.rgo.ru/jour/article/view/2609

- Made 5 different team of analysts focus on impact they could bring to the company. Every team improved KPIs by 3-8% every half a year-Created key dashboards and models: Retention, User Segmentation, Forecasting, LTV etc. Thanks to retention and forecasting models decision makers felt confident and were able to increase DAU by a factor of 2.- Launched several experiments on importance of trial-period length for user retention. As a result revenue went up by 13-15% for a selected subset of users.- Fixed issues with data quality and consistency therefore decreased decision time from a period of 1-2 weeks to the same day. Given a highly competitive market of online streaming that change had a significant impact on our operations.- Introduced daily process of evaluating growth opportunities and causes of decrease in subscribers. Shortened the time of fixing incidents on business side from a week to a couple of days.- Conceived a project to improve ETL pipelines (we've processed petabytes of data). As a result KPIs were calculated by 8am instead of 1pm and product and marketing teams made faster decisions on marketing spent and in-product offers.

- Reorganised analytical processes to identify projects with the most impact and deliver results on time. As a result team started to work 20% faster and prioritise projects with actual value.- Built a marketing impact models for Russian, European and USA markets. Saved several hundred thousands USD per month by correcting marketing strategy and identifying channels with better ROI- Oversaw an in-game items recommendation project (as a project manager). Project demonstrated an increase in ARPU by several percents.

- Implemented a service responsibe for collecting and processing data from various advertising platforms. By using this service marketing teams from different internal subdivisions were able to identify growth opportunities. I estimate the results were up to +10% ROI after the first couple of months. - Established an advertising modelling team that was responsible for estimating an impact of various advertising channels on product KPIs. The team were able to identify best channels and TV slots andincrease overall efficiency by 20-30%. Later, an in-house modelling library was developed by the team to decrease time to build a model from 2-3 months to 3-4 weeks- Conceived an implementation of RFM-analysis tool. The tool was extensively used by performance marketing team and I estimate the results up to +5% per month in term of ROI.