About me:

I am a skilled Data Scientist and Machine Learning Expert with 6+ years of experience in delivering successful commercial projects across multiple industries, including e-commerce, consumer goods, retail, and marketing services. I am proficient in managing the entire project lifecycle, from defining the project scope and methodology to delegating responsibilities, implementation, and deployment, and (oversaw cross-functional teams of up to 6 people).

Currently, I am the Team Leader of the Retail Consulting Analytics Team within the Intelligence Analytics Department. My team focuses on cutting-edge advanced analytics projects related to Data Science and Machine Learning. Under my leadership, my team was able to create and deliver the most expensive ad-hoc project in the history of the company for our platinum client PepsiCo. As a result of our work, I was awarded the Simply Excellent Gold Award 2021 and was included in the company's talent pool.

In my free time, I'm passionate about giving back to the community by volunteering as a Mentor in several global programs. I take great pride in being a part of initiatives such as the Women In Tech Global Movement and Women in Big Data that empower women worldwide and encourage their growth and success. Additionally, I'm a Facilitator in the international Google initiative #IamRemarkable, conducting workshops where I help participants develop their self-presentation skills. Based on feedback from attendees, I was awarded the Silver Tier status.

Key Achievements / Projects in my current role:

I held a hybrid position where I split my time between project management and coding. Approximately 60% of my time was dedicated to coding, where I was responsible for end-to-end development, testing, and deployment. As part of my role, I reviewed code to ensure quality and consistency. I also ensured that the code was well-documented to facilitate future maintenance and development. As the project manager, I oversaw all stages of project development, from scoping to deployment, during the remaining 40% of my time. This included delegating tasks, providing regular status updates, and ensuring that end-to-end development was completed successfully.

• Led a geo-analytics partnership project for PepsicoCo. Designed and implemented a unique methodology that helps our client to identify stores with high growth potential. Received Simply Excellent Gold Award for outstanding performance.
• Launched a new direction in advanced analytics based on transactional data. Designed and developed the first working prototype (MVP) with Python and Tableau. These solutions allow our clients to study customers' behavior, identify store profiles and optimize the assortment. Elaborated on the future strategy for product monetization, and designed the data pipeline architecture.
• Redesigned and optimized pricing optimization products (Weekly Price Monitoring, Most Common Prices, and Retail Price Analytics) resulting in successful pricing strategies and profit optimization for clients including Metro, X5, and Magnit, based on analysis of promotional activities and price elasticities.
• Developed a pilot project to accurately assess future store-level sales at the total food and category level using geo-analytics and time series analysis, achieving a model accuracy rate of 85% to 98% at the store level.

My skills:

• Solid knowledge of Python and R;
• Solid knowledge of SQL: Apache Hive, Cloudera Impala, Microsoft SQL Server, PostgreSQL;
• Familiar with different Machine Learning Algorithms and Statistical Methods such as:

Unsupervised Learning: t-SNE, PCA , NMF, Hierarchy clustering, K-Means clustering;

Supervised Learning: Mixed Effects Models (HLM) Generalized linear model (GLM), SVM, kNN (k-Nearest Neighbor), XGBoost, LightGBM, AdaBoost, Linear Programming (LP);

Deep Learning: Multilayer Perceptron (MLP), Advanced Convolutional Networks (CNN) (ResNet, Inception), Residual Networks, Advanced Recurrent Networks (RNN) (GRU, LSTM);

Time Series Analysis: ARIMA, SARIMA, SARIMAX, VARMA, SES, VARMAX, HWES, ARCH, GARH;

Parametric / Nonparametric statistical methods: Z-test, Students' t-test, ANOVA, Chi-Squared test, Mann-Whitney U Test, Wilcoxon Signed-Rank Test, Kruskal-Wallis H Test, Friedman Test.

• Able to build reproducible ML pipelines and deploy ML models: Docker Containers, Apache AirFlow, Fast API;
• Have strong expertise in various data preprocessing techniques:

Feature preprocessing/engineering: missing data imputation, categorical encoding, variable transformation, outlier-engineering, feature scaling, engineering mixed variables, resampling methods (imbalanced data);

Feature selection: filter methods, wrapper methods, embedded methods, hybrid feature selection methods;

Data Privacy and Anonymization with Python and R;

• Able to work with the main visual tool and libraries like matplotlib, seaborn, ggplot2, Bokhen, Dash Plotly;
• Familiar with different BI systems like Tableau and Power BI (DAX);