Karol is available for hire

Karol Ďuriš

Verified Expert in Engineering

Data Scientist and Developer

Zalužice, Košice Region, Slovakia

Toptal member since February 25, 2026

Expertise

Data Science Data Analysis Database Machine Learning Algorithms Data Engineering SQL Python MySQL Git Mathematics

Bio

Karol is a data scientist with 7+ years of experience delivering ML solutions across fintech, banking, travel, and industrial domains. With a focus on predictive modeling, customer analytics, and data-driven decision support, Karol has proven results such as doubling ancillary sales and improving hiring success rates.

Portfolio

Vacuumlabs

Python, Machine Learning, SQL, R, Pandas, NumPy, Google Cloud Platform (GCP)...

auxmoney

Python, SQL, Machine Learning, Predictive Modeling...

Datatree

Python, SQL, Machine Learning, Data Science, Forecasting...

Experience

Statistics - 15 years
Applied Mathematics - 15 years
SQL - 11 years
Pandas - 7 years
Jupyter Notebook - 7 years
Machine Learning - 7 years
Python - 7 years
R - 3 years

Preferred Environment

Windows, Jupyter Notebook, Cursor AI, Slack

The most amazing...

...thing I’ve created is a recruitment scoring model that improved hiring success from 22% to 46% using candidate data.

Work Experience

Data Scientist

2020 - PRESENT

Vacuumlabs

Performed feature engineering and selection across large datasets (100s of features) to improve model robustness and interpretability.
Led a 2-person data science team to develop predictive models for detecting wound coils improperly on industrial TVA winding machines.
Developed a predictive scoring model that increased internal recruiting success rate from 22% to 46%.
Developed ML modules to enhance SME/micro-credit scoring and credit-limit setting processes.
Developed transaction categorization logic for Indian retail banking data, enabling automated classification across multiple expense categories.
Extracted and structured financial advisor client data from PDF statements using automated data-processing pipelines.
Designed and developed a company-wide reporting system consolidating financial, employee, and client data into a centralized analytical platform.

Technologies: Python, Machine Learning, SQL, R, Pandas, NumPy, Google Cloud Platform (GCP), Scikit-learn, Matplotlib, Docker, Website Data Scraping, Data-informed Recommendations, Data Science, Risk Models, Risk Modeling, Beautiful Soup, n8n, Large Language Models (LLMs), Artificial Intelligence (AI), AI Agents, AI Model Training, Forecasting, Probabilistic Modeling, Linear Regression, Regression Modeling, Feature Engineering, Statistics, Statistical Analysis, Agentic AI, Anthropic, Prompt Engineering, XGBoost, Vertex AI, Data Analysis, Data Analytics, Data Engineering, Databases, Sales Forecasting, ETL, Logistic Regression, Web Scraping, PyCharm, Cursor AI, Data Cleaning, Statistical Learning, Statistical Modeling, Data Modeling, Classification, Git, Kubeflow, Snowflake, Algorithms, Natural Language Processing (NLP)

Data Scientist

2024 - 2025

auxmoney

Contributed to the development of price elasticity models to quantify customer sensitivity to interest rates and predict conversion under different pricing scenarios.
Built ML models to predict loan conversion probability using partner behavioral and financial data.
Designed and automated monitoring of key pricing KPIs, ensuring model stability and early detection of performance drift.
Conducted ad-hoc analyses that identified key profitability drivers.
Developed price elasticity models to quantify customer sensitivity to interest rates and predict conversion under different pricing scenarios.
Built and improved regression-based models for pricing decisions, incorporating partner data and behavioral features.

Technologies: Python, SQL, Machine Learning, Predictive Modeling, Data-informed Recommendations, Data Science, Risk Models, Risk Modeling, Forecasting, Probabilistic Modeling, Pricing Elasticity, Linear Regression, Regression Modeling, Feature Engineering, Statistics, Statistical Analysis, XGBoost, Data Analysis, Data Analytics, Databases, Sales Forecasting, Pricing Models, Logistic Regression, Data Cleaning, Data Modeling, Classification, Git

Data Scientist

2018 - 2019

Datatree

Developed algorithms to assess clients’ financial health, defining and implementing key metrics such as financial reserve and cash-flow stability indicators.
Designed and validated outputs of an automated financial advisory system, ensuring accuracy and reliability of personalized recommendations.
Built transaction categorization logic for card payments in the absence of MCC codes and implemented bank transfer classification by detecting recurring payment patterns and behavioral signals.

Technologies: Python, SQL, Machine Learning, Data Science, Forecasting, Probabilistic Modeling, Linear Regression, Feature Engineering, Statistical Methods, Statistical Analysis, Data Analysis, Data Analytics, Databases, Data Cleaning, Data Labeling, Data Modeling, Algorithms

Data Analyst

2015 - 2018

National Bank of Slovakia

Developed macroeconomic analyses and medium-term forecasts to assess fiscal sustainability and public finance risks.
Analyzed long-term sustainability of the Slovak pension system, incorporating Eurostat demographic projections and aging population dynamics.
Processed and integrated data from multiple economic and administrative sources to support macroeconomic analyses and public finance forecasting.

Technologies: R, MATLAB, SQL, Data Analysis, Data Analytics, Databases, Data Cleaning

Experience

LLM-powered Investment Assistant for Portfolio Insights

Developed a proof-of-concept LLM-powered assistant designed to answer user questions about portfolio performance, such as “Why is my portfolio down today?”

The system leveraged LangChain to orchestrate API calls for retrieving market data and financial news, enabling context-aware responses grounded in real-time information. I implemented query classification and parameter extraction (e.g., time horizon, assets of interest) to dynamically route requests and retrieve relevant data.

The assistant combined structured financial data with unstructured news signals to generate meaningful, user-friendly explanations of portfolio movements.

ML-Ancillary Revenue Optimization

Developed a machine learning–driven system to identify customers most likely to purchase ancillary products (e.g., baggage, insurance) in the post-booking phase for a flight ticket provider. I focused on customer segmentation and campaign optimization, analyzing behavioral patterns to determine the most effective targeting and communication strategies. This included evaluating different marketing channels and measuring customer response across experiments.

ML-based Candidate Scoring System

I developed an automated data pipeline and ML-based scoring system to evaluate developer candidates using publicly available data sources. I engineered structured candidate features and built a predictive model estimating the probability of passing the internal hiring process.

The model increased recruiting effectiveness from 22% to 46% by prioritizing high-potential candidates and identifying key attributes associated with hiring success. The system improved sourcing efficiency and enabled more data-driven screening decisions.

Loan Pricing & Price Elasticity Modeling

I developed predictive models to estimate loan conversion probability within a consumer lending platform. I leveraged partner behavioral and financial data to optimize interest rate strategies and improve portfolio profitability. As part of the customer lifetime value and pricing team, I engineered features from multisource datasets and built models to quantify customer sensitivity to interest rate changes.

Industrial Defect Prediction

I led a data science initiative to detect improperly wound industrial coils using sensor data from TVA winding machines. I managed a 2-person team responsible for processing high-frequency machine sensor data, performing exploratory analysis, and building predictive models to identify defect-prone components.

Automated Financial Health Scoring & Advisory Platform

Developed financial health scoring algorithms powering an automated advisory platform for retail banking clients. I implemented complex transaction categorization logic for card payments without MCC codes and designed pattern-detection algorithms to classify bank transfers. Additionally, I defined and calculated financial health metrics, such as financial reserves and cash flow stability. The system delivered personalized financial recommendations based on income–expense dynamics and portfolio composition.

Education

2013 - 2015

Master's Degree in Informatics and Applied Mathematics

Univerzita Komenského v Bratislave - Bratislava, Slovakia

2010 - 2013

Bachelor's Degree in Informatics and Applied Mathematics

Univerzita Komenského v Bratislave - Bratislava, Slovakia

Skills

Libraries/APIs

Pandas, Scikit-learn, Beautiful Soup, XGBoost, Imbalanced-learn, NumPy, Matplotlib, PyTorch

Tools

Git, Slack, PyCharm, MATLAB, n8n, BigQuery, Claude Code, GIS

Storage

Databases, MySQL

Languages

SQL, Python, R, C++, Snowflake

Frameworks

LightGBM

Platforms

Jupyter Notebook, Windows, Google Cloud Platform (GCP), Docker, Vertex AI, Databricks, Kubeflow

Paradigms

Anomaly Detection, ETL

Other

Statistics, Data Science, Linear Regression, Regression Modeling, Statistical Analysis, Data Analysis, Data Analytics, Data Cleaning, Data Modeling, Analytical Thinking, Analysis, Mathematics, Mathematical Finance, Machine Learning, Applied Mathematics, Risk Models, Risk Modeling, Data-informed Recommendations, Forecasting, Probabilistic Modeling, Logistic Regression, Feature Engineering, Pricing Elasticity, Pricing Models, Data Engineering, Sales Forecasting, Model Evaluation, Data Labeling, Statistical Learning, Statistical Modeling, Classification, Algorithms, Cursor AI, Economics, Econometrics, Statistical Methods, Predictive Modeling, A/B Testing, Customer Lifetime Value (CLV), Financial Modeling, Recommendation Systems, Web Scraping, Website Data Scraping, Large Language Models (LLMs), Artificial Intelligence (AI), AI Agents, AI Model Training, Time Series Forecasting, LangChain, Agentic AI, Anthropic, Prompt Engineering, Vector Databases, API Integration, APIs, Time Series, Time Series Analysis, Machine Learning Operations (MLOps), ETL Pipelines, GeoPandas, Natural Language Processing (NLP)

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