Pavel Logacev, Developer in Berlin, Germany
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Pavel Logacev

Data Scientist and AI Developer

Berlin, Germany

Toptal member since January 27, 2022

Expertise
Data ScienceData VisualizationData AnalysisFull-stackMachine LearningAnalytics DevelopmentRPythonNumPySQLNLPRStudio Shiny
Bio

Pavel is a data scientist specializing in Bayesian methods. He has a master's degree in computational linguistics and a PhD from Potsdam University in Germany. Pavel has over 10 years of experience in statistical data analysis and data science, having worked in sectors as diverse as pricing, psychology, finance, education, health, eCommerce, SEO, and betting markets.

Portfolio

Sell Smart
Python, JavaScript, React, NumPyro, TensorFlow, Feature Engineering...
Pearson Pricing
Google BigQuery, BigQuery, R, Python, dbplyr, SQL, Statistical Modeling...
Freelance
Data Science, R, Tidyverse, Ggplot2, Statistics, Bayesian Statistics...

Experience

  • R - 15 years
  • Ggplot2 - 10 years
  • Data Science - 7 years
  • Bayesian Inference & Modeling - 7 years
  • Machine Learning - 5 years
  • Statistics - 5 years
  • Stan - 5 years
  • Python - 5 years

Preferred Environment

Ubuntu, R, Tidyverse, Ggplot2, PyMC, Python, Pandas, Stan, Polars

The most amazing...

...thing I've worked on is maximizing the sustainability and efficiency of heating systems using heat demand forecasting based on weather forecasts.

Work Experience

Data Scientist

2023 - PRESENT
Sell Smart
  • Developed an automatic price optimization model based on sales data with regular stockouts.
  • Developed a demand forecast model based on sales data with regular stockouts.
  • Built an inventory alerting mechanism based on a probabilistic demand forecast model.
  • Implemented an autonomous pipeline that automatically alerted Shopify merchants if they risked running low on stock.
Technologies: Python, JavaScript, React, NumPyro, TensorFlow, Feature Engineering, Pricing Elasticity, Pricing Models, Forecasting, Data Engineering, Large Language Models (LLMs), eCommerce, ChatGPT, OpenAI, Data Preprocessing, Large Data Sets, B2B, Data Cleaning, Datasets, Communication, Model Deployment, Model Development, Claude Code, JSON, SpaCy

Senior Data Scientist

2022 - PRESENT
Pearson Pricing
  • Carried out price elasticity analyses on big data to determine the optimal price points for stores and products and presented the results.
  • Conducted basket analyses on big data to determine popular combinations of items customers tend to purchase.
  • Created a standardized workflow for price elasticity analysis in BigQuery, which can be applied to large datasets from various industries.
  • Trained analysts in the use of R for data analysis.
Technologies: Google BigQuery, BigQuery, R, Python, dbplyr, SQL, Statistical Modeling, Time Series, Data Analytics, Data Science, Analytics, Predictive Modeling, Regression Modeling, Business Analysis, Software Development, Software Engineering, Linear Regression, Mathematics, Predictive Analytics, Matplotlib, Data Engineering, Big Data, Big Data Architecture, Pricing, Cost Reduction & Optimization (Cost-down), Revenue Optimization, RStudio, NumPyro, Bayesian Inference & Modeling, PyTorch, Statistical Analysis, Applied Statistics, Modeling, Statistical Methods, Full-stack Development, Applied Mathematics, Pricing Models, Data Management, Databases, MySQL, Presentations, Data Pipelines, API Integration, ARIMA, SARIMA, LightGBM, Product Forecasts, Joblib, Demand Forecasting, Retail & Wholesale, Time Series Forecasting, Google Cloud Platform (GCP), Time Series Analysis, Data, Supervised Learning, Unsupervised Learning, Dynamic Pricing, Gradient Boosting, Live Pricing, ETL Pipelines, Model Validation, PostgreSQL, StatsModels, ETL, Probabilistic Modeling, Vertex AI, Feature Engineering, Pricing Elasticity, Amazon Web Services (AWS), Large Language Models (LLMs), ChatGPT, OpenAI, Data Preprocessing, Large Data Sets, B2B, Data Cleaning, Datasets, Communication, Model Deployment, Model Development, Model Evaluation, Stakeholder Management, Causal Inference, Google Cloud Storage, Claude Code, JSON

Data Scientist

2015 - PRESENT
Freelance
  • Implemented machine learning solutions that helped clients make sense of data and automate forecasting processes.
  • Provided statistical consulting and carried out statistical analyses, contributing to scientific publications.
  • Solved numerous continuous and discrete optimization problems.
Technologies: Data Science, R, Tidyverse, Ggplot2, Statistics, Bayesian Statistics, Machine Learning, Bayesian Inference & Modeling, Stan, RStudio Shiny, Rcpp, Regression, Hypothesis Testing, Data Analysis, Data Visualization, Scientific Data Analysis, SQL, REST, H20, C++, C, Git, Statistical Data Analysis, Time Series, XGBoost, Scikit-learn, NumPy, Data Analytics, Analytics, Predictive Modeling, Regression Modeling, Software Development, Software Engineering, Linear Regression, Mathematics, Sports, Predictive Analytics, Forecasting, Matplotlib, Statistical Modeling, Sales Forecasting, RStudio, Python, Fantasy Sports, Statistical Analysis, Applied Statistics, Modeling, Statistical Methods, Full-stack Development, JavaScript, Applied Mathematics, Databases, MySQL, Presentations, Data Pipelines, API Integration, Docker, ARIMA, SARIMA, LightGBM, Product Forecasts, Joblib, Demand Forecasting, Retail & Wholesale, Time Series Forecasting, Time Series Analysis, Data, Supervised Learning, Unsupervised Learning, Gradient Boosting, ETL Pipelines, Model Validation, PostgreSQL, ETL, A/B Testing, Probabilistic Modeling, Vertex AI, Feature Engineering, Computer Vision, Object Detection, OpenCV, Pytest, Streamlit, Data Preprocessing, Large Data Sets, Data Cleaning, Datasets, Communication, Model Deployment, Model Development, Model Evaluation, Random Forests, Causal Inference, Deep Learning

Assistant Professor

2016 - 2022
Bogazici University
  • Designed and taught courses on research methods and statistics.
  • Created new statistical models of eye movements during sentence comprehension in reading.
  • Planned and carried out experimental research on reading and managed an eye-tracking laboratory.
  • Supervised the research of several students, including master's theses.
Technologies: Bayesian Statistics, Bayesian Inference & Modeling, R, Stan, Data Analysis, Tidyverse, Ggplot2, Linguistics, Statistics, Machine Learning, Cognitive Science, Regression, Hypothesis Testing, Data Visualization, Scientific Data Analysis, Data Science, C++, C, Git, Data Analytics, Regression Modeling, Linear Regression, Mathematics, Statistical Modeling, RStudio, Python, Statistical Analysis, Applied Statistics, Modeling, Statistical Methods, Applied Mathematics, Presentations, Data, Supervised Learning, Gradient Boosting, Probabilistic Modeling, Feature Engineering, Amazon Web Services (AWS), Data Preprocessing, Data Cleaning, Communication, Model Development, Model Evaluation, Random Forests, Causal Inference

Data Scientist and R Developer

2015 - 2017
Procter & Gamble
  • Designed and implemented a custom algorithm to automatically determine the main drivers of change in sales and automatically report the key changes.
  • Developed the computational architecture and infrastructure for the analytics solution.
  • Managed a team of 4 data scientists in developing the product.
Technologies: R, Analytics, Feature Engineering, Data Preprocessing, Data Cleaning, Model Evaluation

Researcher

2008 - 2015
University of Potsdam
  • Taught courses on experimentation and research methods.
  • Conducted research on formal models of sentence comprehension.
  • Managed an SQL database with linguistically annotated historical texts.
Technologies: Bayesian Statistics, Bayesian Inference & Modeling, R, Data Analysis, Tidyverse, Ggplot2, Linguistics, Computational Linguistics, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Statistics, Stan, Rcpp, Regression, Hypothesis Testing, Data Visualization, Scientific Data Analysis, Git, Data Analytics, Regression Modeling, Linear Regression, Mathematics, Statistical Modeling, Python, Statistical Analysis, Applied Statistics, Modeling, Statistical Methods, Applied Mathematics, Presentations, Data, Supervised Learning, Gradient Boosting, Data Labeling, Data Preprocessing, Model Evaluation

Software Developer

2001 - 2004
adisoft AG
  • Developed mobile communication solutions on embedded platforms like Symbian operating system.
  • Built web applications in Python for the Zope CMS.
  • Restructured and ported software from Linux to Symbian operating system.
Technologies: Perl, C, C++, Python, Software Development, Software Engineering

Experience

Energy Consumption Prediction

A system for predicting energy consumption based on weather forecasts.

Based on weather data, I created a REST API for forecasting the daily by-block energy demand distribution. The quantile regression model was implemented on H2O, and the REST API in R, using the plumber library.

The predicted distribution of energy consumption was used in simulations serving to optimize the energy efficiency of the district heating system.

Genetic Programming-based Trading System

A genetic programming-based framework for optimizing buy-and-sell signals in an automatic trading system.

I developed and implemented the logic of the optimization algorithm in R and backtested on historical data. Signals were exported in a MetaTrader readable format.

Churn Prediction App

An app for churn prediction. I implemented an app with an RShiny web interface that lets the user upload a tabular dataset in several formats and, given some metadata, automatically trains multiple customer churn models for a new dataset.

The app displays predictions of churn probability and features importance values.

Price Optimization for a Major Fast-food Chain in Europe

As a technical lead, I designed and delivered an end-to-end pricing optimization for a major fast-food chain covering around 300 stores across France. The project addressed margin erosion by replacing ad-hoc pricing decisions with a rigorous, data-driven approach.

I built a multi-level elasticity model using POS data, capturing nonlinear price-response relationships in log-log space, and explicitly estimating elasticities and cross-elasticities between different meal sizes. This enabled us to jointly optimize prices for each size triplet, balancing upsell incentives and margin targets. After scenario analysis and alignment with the client’s pricing team, we delivered an actionable optimized price list—about 70% of which was adopted chain-wide. The entire project, from model design to stakeholder handover, was completed in six weeks.

Sell Smart: Price Optimization

http://sell-smart.app
A Shopify app for price optimization. I developed the front and back ends of the app, which ingest a merchant's Shopify sales and product data, calculate price elasticity of demand, and suggest better prices within business constraints.

Education

2009 - 2015

PhD in Cognitive Science

University of Potsdam - Potsdam, Germany

2003 - 2008

Master's Degree in Computational Linguistics

University of Potsdam - Potsdam, Germany

Skills

Libraries/APIs

Ggplot2, Pandas, NumPy, Tidyverse, Scikit-learn, Joblib, PyMC, Rcpp, Matplotlib, XGBoost, PyTorch, React, TensorFlow, Shopify API, OpenCV, SpaCy

Tools

ARIMA, SARIMA, StatsModels, ChatGPT, Claude Code, Git, BigQuery, Prefect, Pytest

Languages

R, Python, SQL, C++, Perl, C, JavaScript

Industry Expertise

Applied Statistics, Retail & Wholesale

Frameworks

LightGBM, RStudio Shiny, Streamlit

Paradigms

ETL, B2B, REST

Platforms

Google Cloud Platform (GCP), Ubuntu, H20, RStudio, Amazon Web Services (AWS), Docker, Vertex AI

Storage

Databases, MySQL, Data Pipelines, PostgreSQL, Google Cloud Storage, JSON

Other

Linguistics, Statistics, Machine Learning, Cognitive Science, Data Science, Scientific Data Analysis, Data Visualization, Data Analysis, Hypothesis Testing, Regression, Data Analytics, Statistical Data Analysis, Statistical Modeling, Analytics, Regression Modeling, Linear Regression, Statistical Analysis, Modeling, Statistical Methods, Full-stack Development, Logistic Regression, Pricing Models, Data, Supervised Learning, Feature Engineering, Pricing Elasticity, Data Preprocessing, Model Development, Random Forests, Natural Language Processing (NLP), Bayesian Statistics, Bayesian Inference & Modeling, Stan, A/B Testing, Time Series, Predictive Modeling, Software Development, Software Engineering, Mathematics, Predictive Analytics, Forecasting, Data Engineering, Big Data, Sales Forecasting, Pricing, Cloud, Fantasy Sports, Applied Mathematics, Data Management, Presentations, Product Forecasts, Demand Forecasting, Time Series Forecasting, Time Series Analysis, Unsupervised Learning, Dynamic Pricing, Gradient Boosting, ETL Pipelines, Model Validation, Probabilistic Modeling, OpenAI, Pricing Optimization, Pricing Strategy, Large Data Sets, Data Cleaning, Datasets, Communication, Model Deployment, Model Evaluation, Causal Inference, Computational Linguistics, Genetic Algorithms, Google BigQuery, dbplyr, Business Analysis, Sports, Big Data Architecture, Cost Reduction & Optimization (Cost-down), Revenue Optimization, Generative Pre-trained Transformers (GPT), NumPyro, Artificial Intelligence (AI), eCommerce, API Integration, Live Pricing, Spatial Analysis, Polars, Computer Vision, Data Labeling, Large Language Models (LLMs), Object Detection, Stakeholder Management, Deep Learning

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