Enes is available for hire
Hire EnesEnes Zvornicanin
Verified Expert in Engineering
Data Scientist and Developer
Location
Sarajevo, Federation of Bosnia and Herzegovina, Bosnia and Herzegovina
Toptal Member Since
August 16, 2021
Enes is a data scientist with over four years of experience, including three years working remotely with US-based teams from The World Bank and a Silicon Valley startup, Yewno. Enes focuses on developing state-of-the-art machine learning solutions using time-series, tabular, and text data. He has experience working on end-to-end ML and data-based projects, including analysis, research, development, and deployment. He is familiar with MLOps and data engineering best practices and tools.
Portfolio
World Bank Group
Python, Pandas, Polars, NumPy, NetworkX, Google Maps API, Bing Maps API...
Self-employed
Machine Learning, Data Science, Natural Language Processing (NLP), Gensim...
Tech 387
Python, Data Visualization, Data Analysis, Business Analysis, Pandas, Plotly...
Experience
Availability
Part-time
Preferred Environment
Slack, Jira, Google Hangouts, Python, Amazon Web Services (AWS)
The most amazing...
...thing I've developed is a stock market prediction system using machine learning techniques.
Work Experience
Data Science Consultant
2022 - PRESENT
World Bank Group
- Worked remotely as a data science consultant on an NLP project with a team based in Washington, DC.
- Developed and optimized a complex system for text cleaning, named-entity resolution, data clustering, and entity linking on very messy text data.
- Worked with tabular geospatial data with a few billion rows. Developed multiple Python scripts to extract and validate geo-specific terms using Google Maps and Bing Maps API.
Technologies: Python, Pandas, Polars, NumPy, NetworkX, Google Maps API, Bing Maps API, Natural Language Processing (NLP), Fuzzy Logic, Unsupervised Learning, Big Data, Slack, Geospatial Data, Machine Learning, Data Science
Freelance Data Scientist | Technical Writer
2021 - PRESENT
Self-employed
- Developed several bio-medical NLP pipelines using Gensim Word2Vec and BERT-type models. Worked on tasks related to NER and topic modeling.
- Built a machine learning credit score system focused on the interpretability of the models. Developed and deployed Python script for crypto trading using Binance API.
- Worked on several projects related to time-series forecasting.
- Wrote around 50 articles and tutorials related to ML and data science topics that are available at Kaggle (kaggle.com/eneszvo), Baeldung (baeldung.com/cs/author/eneszvornicanin), and Neptune.ai (see more in the projects).
Technologies: Machine Learning, Data Science, Natural Language Processing (NLP), Gensim, Hugging Face, Pandas, StatsModels, ARIMA, Keras, TensorFlow, PyTorch, Python, Jupyter Notebook, Git, SQL, SQLite, Scikit-learn, SHAP, XGBoost, Binance API, Amazon EC2, Amazon S3 (AWS S3), Unix, NumPy, Data Analysis, Statistical Modeling, Deep Learning, Artificial Intelligence (AI), Data Engineering
Senior Data Scientist
2021 - 2022
Tech 387
- Analyzed data from a mobile app and extracted business metrics, including conversion rate, CPU, user engagement, user segmentation, and survival analysis. Identified possible issues and recommended a business strategy for future development.
- Developed and deployed a system that helps project managers to track Jira tickets. The system uses data from Jira API, sends notifications and alerts through Slack bots and email, and shows charts on a website that is deployed on AWS using Flask.
- Developed web scraping Python scripts and organized internal data in the company.
- Created several Slack bots and ETL processes using Python.
Technologies: Python, Data Visualization, Data Analysis, Business Analysis, Pandas, Plotly, Flask, Matplotlib, Mixpanel API, Google Cloud Platform (GCP), Amazon EC2, Jira REST API, Slack API, Firebase, Git, Cron, Jupyter Notebook, Selenium, SQL, Data Analytics, Data Science
Lead Data Scientist
2020 - 2021
Entropy387 (Yewno)
- Developed and trained over one million diverse machine learning models for stock market movement prediction, which improved the trading strategy by approximately 30% in cumulative return.
- Applied data preprocessing, feature engineering, and optimization techniques for tuning ML models.
- Collaborated closely with high-level colleagues from finance and business to brainstorm ideas for new products and improvements.
Technologies: Machine Learning, Artificial Intelligence (AI), Python, Time Series, Data Science, Classification, Regression, Optimization, Data Cleaning, Pandas, Scikit-learn, Keras, NumPy, Stock Analysis, Jira, Git, Econometrics, Networks, Matplotlib, Data Analysis, Modeling, Amazon Web Services (AWS), Jupyter, Amazon S3 (AWS S3), Data Analytics, Trading, Statistical Modeling, Microsoft Excel, Jupyter Notebook, Natural Language Processing (NLP), Deep Learning, Neural Networks, Statistics, XGBoost, TensorFlow, Slack, Google Hangouts, Linux, PyCharm, ETL, Algorithms
Data Scientist
2019 - 2020
Entropy387 (Yewno)
- Conducted research and developed several state-of-the-art solutions using graph embedding techniques, network anomaly detection methods, and reinforcement learning.
- Prepared and held presentations regarding data QA, cleaning, and analysis using Jupyter notebooks.
- Built several ETLs and collaborated closely with the data engineering team preparing data pipelines for production.
Technologies: Python, Machine Learning, Optimization, Artificial Intelligence (AI), Algorithms, Programming, SQL, Time Series, Data Science, Classification, Data Cleaning, Pandas, Scikit-learn, Keras, NumPy, Clustering, Stock Analysis, Jira, Git, Networks, Matplotlib, Data Analysis, Jupyter Notebook, Slack, Google Hangouts, Linux, PyCharm, Modeling, Amazon Web Services (AWS), Jupyter, Amazon S3 (AWS S3), Data Analytics, Microsoft Excel, ETL
Data Scientist
2018 - 2019
Cape Ann Enterprise
- Researched and developed several neural networks and machine learning methods for classification.
- Implemented a pipeline for NLP tasks similar to sentiment analysis.
- Prepared and cleaned data for classification tasks.
Technologies: Python, Jupyter Notebook, Keras, Pandas, Natural Language Processing (NLP), Data Science, Machine Learning, Deep Learning, Neural Networks, Modeling, Jupyter, Data Analysis, Microsoft Excel, Artificial Intelligence (AI), Programming, Classification, Data Cleaning, Matplotlib, Linux, PyCharm, Data Analytics, Algorithms
Experience
Building MLOps Pipeline for Time Series Prediction [Tutorial]
https://neptune.ai/blog/mlops-pipeline-for-time-series-prediction-tutorialThis tutorial describes and implements an end-to-end time-series project based on the Binance trading app, utilizing MLOps architecture. The project follows best CI/CD practices and incorporates technologies like GitHub Actions, Docker, Amazon ECR, ECS, EC2, S3, Neptune API, XGBoost, Optuna, cron jobs, and more.
Overview of Time-series Forecasting Methods
https://www.kaggle.com/eneszvo/time-series-forecasting-p1-es-arima-varThe purpose of this project is to provide a simple and clear theoretical explanation and minimal working examples of several models for time series forecasting from econometrics. Besides that, in the Jupyter notebook are explained some terms such as stationarity, ACF, PACF, and so on.
Deploying ML Models: How to Make Sure the New Model Is Better Than the One in Production?
https://neptune.ai/blog/deploying-ml-models-make-sure-new-model-is-betterThis article describes how to ensure that a new ML model is better than the one currently in production. It explains when it is necessary to deploy a new model in production and discusses the most popular metrics for comparison. It also describes some of the most popular deployment techniques, such as shadow deployment, A/B testing, canary deployment, and feature flags.
Automated Testing in Machine Learning Projects [Best Practices for MLOps]
https://neptune.ai/blog/automated-testing-machine-learningThe article on automated testing in machine learning explains the types of automated tests, including smoke testing, unit testing, integration testing, regression testing, data testing, model testing, and monitoring testing.
Topic Modeling and Latent Dirichlet Allocation (LDA)
https://datascienceplus.com/topic-modeling-and-latent-dirichlet-allocation-lda/This article explains the natural language processing technique for topic modeling using latent Dirichlet allocation (LDA). Besides the theoretical part, there is a simple tutorial in Python with some NLP preprocessing techniques.
Shopee Summary—Matching Products Using Images and Titles
https://www.kaggle.com/eneszvo/shopee-summary-efficientnet-arcface-bertThis project is a summary of the "Shopee—Price Match Guarantee" competition where the main goal was to match the same products based on their images and titles. In this, a Jupyter notebook presented an overview of different methods for detect similar images or sentences using pre-trained neural network embeddings, pHash, TF-IDF, and similar.
Time Series Projects: Tools, Packages, and Libraries That Can Help
https://neptune.ai/blog/time-series-tools-packages-librariesThis article presents the most popular Python packages and libraries that can be used in any time-series project. Also, the concept of time series and some examples are introduced. In the end, is presented a comparison of these packages.
Introduction to Crypto Bitcoin Trading with Python and Binance
https://medium.com/insiderfinance/introduction-to-crypto-bitcoin-trading-with-python-and-binance-743916258e5fThis article explains how to download historical data from Binance, build a simple trading strategy with Python, backtest and optimize it, and finally deploy it on AWS. All code is shared in the GitHub repository.
Education
2018 - 2020
Master's Degree in Computer Science
University of Sarajevo, Faculty of Natural Sciences and Mathematics - Sarajevo, Bosnia and Herzegovina
2014 - 2018
Bachelor's Degree in Applied Mathematics
University of Tuzla, Faculty of Natural Sciences and Mathematics - Tuzla, Bosnia and Herzegovina
Skills
Libraries/APIs
Pandas, Scikit-learn, Matplotlib, NumPy, XGBoost, Keras, TensorFlow, Mixpanel API, Jira REST API, Slack API, PyTorch, Binance API, NetworkX, Google Maps API, Bing Maps API
Tools
PyCharm, Jupyter, Jira, Slack, Google Hangouts, Microsoft Excel, Git, Plotly, Cron, Gensim, StatsModels
Languages
Python, SQL
Paradigms
Data Science, ETL
Platforms
Jupyter Notebook, Linux, Amazon Web Services (AWS), Google Cloud Platform (GCP), Amazon EC2, Firebase, Unix, Visual Studio Code (VS Code), Docker
Frameworks
Flask, Selenium
Storage
Amazon S3 (AWS S3), SQLite
Other
Machine Learning, Optimization, Applied Mathematics, Programming, Time Series, Classification, Data Cleaning, Data Analysis, Natural Language Processing (NLP), Statistics, Modeling, Data Analytics, Artificial Intelligence (AI), Algorithms, Regression, Clustering, Stock Analysis, Econometrics, Networks, Deep Learning, Neural Networks, Statistical Modeling, Unsupervised Learning, BERT, Image Processing, Trading, Object Detection, Video Processing, Data Visualization, Business Analysis, Hugging Face, ARIMA, SHAP, Data Engineering, Machine Learning Operations (MLOps), Deep Neural Networks, Convolutional Neural Networks (CNN), Polars, Fuzzy Logic, Big Data, Geospatial Data, CI/CD Pipelines, Neptune, Deployment
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