Position: Data Scientist / Data Analyst
Category: Data Science
English level: fluent
Years of experience: 2.5

-Recommendation system, increased click-through rate by > 20%. All the conventional approaches, including collaborative filtering, item-based and hybrid algorithms, were failing in the case of C2C marketplace, where the lifetime of ads is very short. It was impossible to collect statistically significant data about a particular ad or user, so we moved from user-ad interaction granularity to user cluster - ad cluster. I was responsible for the clusterization of users, which was done based on their interactions as clicks, views and contacts with different categories of good. I tried several unsupervised learning algorithms, and Latent Dirichlet Allocation turned out to be the best in terms of business interpretability. This clusterization showed us some interesting segments of users including 'people who just moved to a new place' (were buying different types of furniture, carpets and some housing furniture) or segment of 'students' (looked for language courses, books, visa services).

- Predicting the speed of sales of products and optimizing it by providing a seller with recommendations for an ad design. XGBoost for predicting hours till the sale; using prices, features from the product description and product picture as variables. Afterwards, we used Shap and Lime explainer to provide the seller with factors that slow down the sale process. The model for sell-time prediction is also used as a proxy for ad quality to sort ads in the recommendation block. The accuracy of a model is nearly 80%, and it is already used in production.

- Survival model for churn prediction with 70% accuracy. The model is build to detect those with a high risk of churn, find the early predictors of churn and act to save customers. Still in the experimental phase.

- Find duplicate products based on the photo for the online marketplace. We implemented an algorithm stacking two-level clusterization and Similarity Network (deep learning), and increased the grouping level by ~30%.
I am a Senior Data Science consultant at a European consulting company. Main responsibilities include Data Science team management, project negotiation and project delivery.

I have 2+ years of experience working in the field, proficiency with statistical analysis (hypothesis testing, parameter estimation, confidence intervals, Monte Carlo, regressions), classical ML models (boosting, bagging, SVM etc.) and Deep Learning(CNN, Similarity Net, LSTM, Binary Networks etc). 

Also, I have 1-year of experience of research work in the Deep Learning field in the top European research centre.
I expect either Data Scientist or Data Analyst position in a start-up or a product company. Project/Team management is a possibility as well.