• Researched and analysed German and French electricity prices in terms of underlying market fundamentals, physical grid constraints and regulatory environment• Developed a quantile regression forests (QRF), predicting the spread btw. day-ahead and intraday prices on the French electricity market

• Analysed and automated (from Java to Python) a model for predicting cardiovascular diseases, based on patients’ ECG data• Drafted offers for analytics solution to potential customers • Interviewed job candidates and reviewed their solutions to a take-home assignment

• Extracted decoded data from blockchain using SQL• Built on-demand dashboards to visualize key DeFi protocol metrics• Set up token economy models, revenue estimate and full economy papers for DeFi projects

• Developed PoC, tested and successfully implemented separate discount pricing models that optimize profit of second hand and outlet product segments on the website(9 countries, 100K+ unique products, 10K+brands).• Proactively combined and analyzed internal, structured and unstructured data and data from third party platforms to suggest improvements in the sales&marketing efforts and in customer journey and shopping experience• Developed PoC models such as customer and product segmentations, CLTV, recommender system and value-based sorting strategy of the 100K+products on the website.• Defined task requirements and was in charge with supervising the implementation and collaboration with the frontend and backend web dev. teams• Guided technically junior Data Scientist and ML Engineer• Project work involved data prep and model development in Python and relied on GCP and BigQuery infrastructure & services

• Analysed user and system data, collected in Chaos Cloud (cloud rendering platform) • Developed ML models to predict individual scene rendering requirements in terms of memory and CPU to predict and optimise rendering cost for the platform and the user• Analysed and translated features and parameters of a scene to be rendered from a json-like format into numeric data and performed feature selection• Gave recommendations on data collection and collaborate with platform developers on devising tools for data retrieval and manipulation • Project work involved data prep and model development in Python • Gave recommendations on data collection and collaborate with developers on devising toolsfor data retrieval and manipulation• project work involves data prep and model development in Python

• PoC in the FinTech industry: − cash withdrawal forecasting of 500+ ATM network of a Bulgarian bank, based on recurrent neural networks (RNN) − cost optimization of cash replenishment services of 500+ ATM network• PoC in the IoT industry: predictive maintenance model using Spark • Project work involved data prep and modelling in R and Python and deployment with MS Azure

• Contributed to the development of the Experian Marketing Analytics&Campaign Management Suite: from ETL processes, data modeling, to data summarisation and visualisation• Followed best practices and developed scorecard and risk models for the FinTech sector• Supported technically junior team members

• Researched and applied statistical and machine learning approaches to standard and custom projects in the marketing analytics domain: propensity-to buy − churn prediction − customer value prediction − recommender systems − customer segmentation • Project work involved data prep and model development in R