lang-uk/recruitment-dataset-candidate-profiles-english huggingface dataset:
Position: Data Scientist
Category: Data Science
Experience: 1 years | English: upper

2021.11 – hack4retail – Organized Fozzy Group and McKinsey & Company
Tasks: 1. prediction of retail network sales (online part); 2. Distribution of goods on shelves for the new network of dark markets.
Our team reached the final, and as a result, took 19th place.

2020.06 Capstone Project of Data Science Specialization (wrote an article on Medium) 
- The aim of this project is to help a prospective investor to find the best location for the opening of a restaurant
Stack: Web scraping (BeautifulSoup), Geospatial data (GeoPy, Folium, API Foursquare), ML (Scikit-Learn, K-Means clustering)

Courses and certificates:
DS, ML: IBM Data Science Specialization by IBM on Coursera 
Python, Computer science: Computational Thinking using Python by MIT on EdX
Math: Applied Mathematics and Statistics for Data Science by Robot Dreams
Deep Learning: Neural Networks and Deep Learning, Improving Deep Neural Networks, Convolutional Neural Networks by DeepLearning.AI on Coursera
I am keen on DS/ML/DL and looking for opportunities to work as a Data Scientist. At the same time, I have huge experience in managing companies in B2B automotive and consumer recreational goods segments.

2020.09 – 2021.09 The largest independent service of online payments and money transfers in Ukraine
- Developing an algorithm to obtain a unique ID for B2C customers
- clustering customers (based on RFM analysis),
- extracting dependencies/insights from the time series
Stack: NetworkX, Scikit-Learn, K-Means clustering

2000.05 – present. Huge experience in managing companies in domain areas: distribution of outboard/inboard engines, boats, yachts, and commercial vehicles spare parts.
I am currently looking for a job as a Junior Data Scientist/ML Engineer. First of all, I am not interested in money, but in gaining real "industrial" experience and meeting interesting people from the field of artificial intelligence.
Position: Data Scientist
Category: Data Science
Experience: 0.5 years | English: upper


I am looking for a Data Scientist position.

I have experience working at a startup, where I validated hypotheses for financial asset price predictions using ML models.

Technical stack:
Programming Languages: Python 3, R
ML: Scikit-Learn, XGBoost, TensorFlow, Keras
Data analysis: Pandas, SciPy, NumPy, MS Excel
Data Visualisation: Altair, Matplotlib, Seaborn, Plotly
Position: Data Scientist
Category: Python
Experience: 6 years | English: fluent


I am mathematician who is doing machine learning projects in wide range. Similarity search, 18+ content detection in images, celebrity tagging, object detection/tracking, offline navigation system creation, anomaly detection, quantitative research projects was done by me. I am using Python as programming language and tools like Pytorch, Tensorflow, working on Linux and Windows. I am open to new interesting projects!
Position: Data Scientist
Category: Data Science
Experience: 1.5 years | English: intermediate


I am motivated Data Scientist and Machine learning enthusiast with a year of experience as a data scientist/analyst role at dierent companies, such as A2Z technologies, Unibank, Egov development center. I have spent my last year as an NLP engineer. Currently, I am working on Automatic Speech Recognition and speaker diarization. I am able to working with different tools  for ASR systems. At the same time, I am into Full stack web development with Django. My experience includes data cleaning and manipulation with using pandas.  I  have some real project experience that will help me building predictive models with ML algorithms for classification problems. I am also able to  measure model's performance using different metrics.
Position: Data Scientist
Category: Data Science
Experience: 0.5 years | English: upper


I am now Machine learning engineer researcher at Ailab company.
Worked in Qss analytics company as Data Scientist. Made models 
for churn analysis, loan prediction, home price prediction, predicting viewer count for webpage using time series analysis, built neural network for CNN tasks such as identifying similar faces to the specific person,I also used big data tools such as hadoop, spark when it is needed.  I used scikit learn, matplotlib, pandas, pytorch,numpy and deployed using Flask in all these task abovementioned. I also scraped data from lots of sites using Scrapy, Beautiful Soup. 
Now I am working on NLP project.
I am looking for all opportunities which suitable to my skills.
Position: Data Scientist
Category: Data Science
Experience: 2 years | English: upper

Courses:
Coursera – Python Basic Data Processing and Visualization byUniversity of California San Diego
Coursera – Tensorflow for AI and Machine Learning bydeeplearning.ai
Coursera – Convolutional Neural Networks in TensorFlow bydeeplearning.ai
Udemy – Python for Data Science and Machine Learning Bootcamp

Achievements:
– Sikorsky Challenge – startup "Greenales pot" (2020)
– Yangel Acceleration – space startup "Greenales" (2020)
I am skilled in machine learning, deep learning, statistics and analytics. Experienced in NLP, and CV tasks. I have certain Technical skills:
- Python, HTML, CSS;
- Data Processing Libraries: NumPy, Pandas, collections, itertools and other distributed processing tools;
- Creation, proof and refutation of hypotheses
- ML/DL libraries: TensorFlow, PyTorch, Transformers, SpaCy, XGBoost, Hugging Face, and other related packages;
- Data visualization libraries: Seaborn, Matplotlib, Plotly;
- Statistical data analysis, creation, verification and proof of hypotheses;
- NLP: Parsing unstructured documents, data mining, entity extraction and sentiment analysis;
- Strong knowledge of semantic analysis algorithms and libraries such as NLTK, Gensim, different variations of BERT, packages and algorithms for embedding extraction, pre-processing techniques, etc;
- Worked with AWS Comprehend, AWS Textract;
- Some experience with SQL queries;
- Experienced in developing microservices using Docker and FastAPI.
- CV: experience with computer vision algorithms and libraries, including OpenCV and YOLO;
- Skilled in working collaboratively with cross-functional teams and effectively communicating complex subjects in simple terms.

Worked on such projects as:
- Created a parser for CVs using variaties of NLP techniques such as parsing, scraping, pre-processing, semantic analysis, syntax-based rules, chanking, NER, sentiment analysis, etc.
- Developed an NLP text tone and semantic analyzer capable of detecting tone and sentiment of text data using text preprocessing, word embeddings, and machine learning algorithms. Utilized NLTK, Gensim, SpaCy, TensorFlow, Keras, BERT, GloVe, Word2Vec, and FastText libraries to achieve high accuracy rates in detecting positive, negative, neutral, fear, disgust, happiness.
- Computer vision tasks, such as image classification, object detection, and segmentation (using algorithms on text documents);
- Utilized computer vision techniques to perform floor area detection in room images. Used Python libraries such as OpenCV, NumPy, and Matplotlib for image preprocessing and analysis. Applied edge detection algorithms and Otsu's thresholding to effectively segment the floor area and enhance accuracy
Position: Data Scientist
Category: Data Science
Experience: 2.5 years | English: upper

1. Implemented robust candidate grading models:
2. Creating skill-assessment tool for a hiring platform.
3. Question recommendation system
4. Complete implementation of Active Learning tool
5. Error prediction model
6. Models monitoring dashboards in Tableau and Chartio
I am well-versed with Machine Learning libraries like PyTorch, Keras and Scikit-Learn. Moreover, I have worked extensively with other general purpose libraries such as pandas, numpy, matplotlib and data analysis platform Tableau.
Position: Data Scientist
Category: Python
Experience: 0 years | English: fluent

I utilized a range of technologies commonly used in data science, including Python for data manipulation and analysis, libraries such as Pandas and NumPy, and machine learning frameworks like scikit-learn. I also gained experience in data visualization using tools such as Matplotlib and Tableau. Additionally, I familiarized myself with SQL for data querying and database management.
I am writing to express my interest in the data scientist position at [Company Name]. As a recent graduate of the data scientist course at Main Academy, I am eager to apply my knowledge and skills in a professional setting.

During my time at Main Academy, I completed several projects and tasks that allowed me to gain hands-on experience in various areas of data science. I worked on projects involving data cleaning, exploratory data analysis, and predictive modeling. These projects provided me with valuable exposure to real-world datasets and the opportunity to apply the techniques and concepts learned during my coursework.
As I embark on my professional journey as a data scientist, I am committed to continuous improvement and growth. I recognize that there is always more to learn in this rapidly evolving field. I am particularly interested in expanding my knowledge in deep learning
Position: Data Scientist
Category: Data Science
Experience: 2 years | English: upper


I began my career as a Software Engineer, gaining proficiency in software development. However, my fascination with Machine Learning led me to shift my focus and explore its vast potential.

At AzAstro, I had the opportunity to work as a Machine Learning Engineer, specializing in analyzing satellite images. My role involved leveraging Machine Learning techniques to extract valuable insights from these images. I applied image processing, computer vision algorithms, and pattern recognition to uncover hidden information within the satellite data.

After my tenure at AzAstro, I joined BOLT as a Data Scientist, working on diverse projects that expanded my skills. Within this role, I created advanced computer vision models, conducted in-depth time series analysis, developed interactive dashboards, and built AI bots. This allowed me to tackle complex challenges and deliver innovative solutions.

Continuing my career progression, I took up a position as a Research Engineer at LUSAC Lab in France. My primary focus was on using Deep Learning models for predicting fuel cell behavior. I applied advanced algorithms and techniques to improve the accuracy and efficiency of fuel cell performance prediction. This involved comprehensive data analysis, designing sophisticated models, training them meticulously, and rigorously evaluating their performance in the domain of fuel cells.

Building on my industry experience, I am now excited to announce that I will be starting a PhD in Machine Learning. This academic pursuit will provide me with an opportunity to delve deeper into the intricacies of the field and contribute to cutting-edge research. I aim to specialize in areas such as satellite image analysis, computer vision, and predictive modeling, further fueling my passion for innovation.

By combining practical experience with academic pursuits, I am committed to making significant contributions to the field of Machine Learning and pushing the boundaries of knowledge.

If you have any further questions or require additional information, please feel free to ask!
Position: Data Scientist
Category: Python
Experience: 1 years | English: fluent

Accomplishments/ Projects can be seen in my CV.
I completed my mechanical engineering studies with a bachelors degree this summer 2021. For the first time I got in touch with Python in the university. So I wanted to learn more about it and completed two Udemy courses, which you can see in my CV.
Right now I am sharpening my skills with Kaggle competitions and I am eager to learn more about the Data Science world.
In an internship I had the opportunity to take some research in implementing an Image Classifier with PyTorch and coded a Machine Vision application.
On top of the internship, I also wrote my thesis about Machine Vision in the same department.
Position: Data Scientist
Category: Data Science
Experience: 2.5 years | English: intermediate


I created a machine learning model that predicts customer churn and a conversion prediction model for businesses. I also created an assortment matrix to optimize inventory and implemented dynamic pricing using NLP techniques. Additionally, I designed and implemented a smart search feature for the company's website. These efforts resulted in significant revenue gains.

I classified subscribers' calls by topics and tone to improve customer service and identified profitable areas for installing 4G stations. I collaborated with business representatives to identify problem areas and developed projects to improve efficiency and profitability.

Led a team that digitalized commercial processes and developed online platforms for two mini markets with advanced functionalities, resulting in a total revenue of $6,675.
Position: Data Scientist
Category: Data Science
Experience: 0 years | English: fluent

1. Developed a comprehensive understanding of data science principles, including data collection, analysis, and visualization.
2. Developed skills in programming languages such as Python for data manipulation and analysis.
3. Utilized machine learning algorithms to identify patterns in large datasets.
4. Created interactive dashboards to visualize data and present insights to stakeholders.
5. Implemented statistical models to forecast trends and predict outcomes from data sets.
6. Developed automated processes for collecting, cleaning, and organizing large datasets for analysis.
7. Collaborated with cross-functional teams to develop strategies for leveraging data-driven insights into business decisions.
8. Leveraged natural language processing techniques to extract meaningful insights from unstructured text data sources.
I created my pet project that I built with Streamlit and Plotly that let's the user create quick and easy visualizations on English Premier League's players and teams.
It allows for anyone to create sophisticated plots and charts and start their sport analysis journey.
I expect a good team with which I can grow in the direction that interests me.
Position: Data Scientist
Category: Python
Experience: 4 years | English: fluent


Identification of the most triggering messages for bank clients. NLP and Analytics
- Led successful completion of full ML pipeline, delivering data-driven insights and solutions to meet business goals
- Implemented and deployed 95% accurate message classification for bank clients
- Reduced call center load by 15% through data-driven analytics and solutions
Technologies: Python, AWS, Git, SQL, Minio, Pandas, Pyspark, Airflow, K-means, scikit-learn

Anomaly Detection in network logs. Cybersecurity
- Developed and deployed crucial anomaly detection model for bank security using Isolation Forest
- Improved model’s recall by 10% through modifications and optimization
Technologies: Python, AWS, Git, SQL, Minio, Pyspark, Hadoop, Airflow, PCA, K-means, Isolation Forest

Prediction of a project’s deadlines. Regression
- Facilitated weekly cross-functional brainstorming sessions resulting in successful collaboration with German business team
- Developed and deployed advanced machine learning models (PyTorch, XGBoost) resulting in 200% increased accuracy over human experts
Technologies: Python, AWS, Git, Bash, SQL, Docker, FastAPI, XGBoost, Pytorch

Anomaly detection in B2B transactions. Time series analysis
- Gained expertise in time series analysis and anomaly detection, trained advanced models resulting in improved predictions
- Deployed the anomaly detection service, resulting in a 30% reduction of false positives and outperforming existing models
Technologies: Python, AWS, SQL, Pandas, Docker, FastAPI, Prophet, Keras, Numpy, Scipy, Statsmodels

Autocompletion service for company users. NLP, Language Modelling
- Developed and deployed an autocompletion service utilizing FastAPI and Docker
- Designed and implemented neural network with attention mechanism, achieving 80% accuracy
Technologies: Python, AWS, SQL, Docker, FastAPI, Pytorch, Keras, LSTM, RNN, NLTK, Fasttext
Position: Data Scientist
Category: Data Science
Experience: 2 years | English: fluent


I designed various deep learning models, in PyTorch and TensorFlow, including one predicting flow fields around airfoils from its geometry, which approximates the solution for navier-stroke equations, and predicting cardiac-vascular diseases from the EEG inputs. I also wrote a discrete optimization tool for PyTorch, as an extension, which is like a combinatorial solvers but differentiable and continuous-relaxed.
Position: Data Scientist
Category: Data Science
Experience: 2 years | English: upper


I developed natural language processing models to improve the mapping process of movies and franchise projects and also engaged in projects with spend allocation and optimization costs, resulting in improved project performance. To ensure the accuracy and reliability of our results, I provided exploratory data analysis (EDA) for our models. Additionally, I designed rule-based algorithms that automated the demanding choice of our customers. In my work in the political and economic research department, I conducted a wide network analysis of political research, providing insights and recommendations for policy decisions. I also engaged in writing academic articles on the intersection of data science and economics. For the educational institution, I provided business intelligence (BI) analysis with visualizations that now help consumers make decisions, provide events to develop the educational process better and show the performance of their students to investors.
Position: Data Scientist
Category: Data Science
Experience: 0 years | English: upper

Coursera certificates for deep learning (studied, not just passed).
I'd like to work more with deep learning models, especially some Computer vision tasks, but I'm ready to study any other stuff.  I'm a student right now, 4th grade. Talking about work, I'm interested in NNs, math, programming.
I hope to find the place where I will get tasks and approximate directions (like make *this*, it should have such input/output, you may use *some tools*). So I will be able to learn how to do it on the go and my inexperience won't be a problem. I'm ready to study any new tools, tricks or any other things.
Position: Data Scientist
Category: Data Science
Experience: 0.5 years | English: upper


I do have a half of a year of experience. The purpose of the first project was to determine the water level in the Crimean reservoirs from satellite images. In the future, you can use this tool with any reservoirs, not necessarily Crimean. This project provided a better understanding of the situation with the amount of drinking water on the peninsula.
At the moment, I am also working on the recognition of objects from satellite images. More precisely, on the classification of the earth's surface into different types. This is necessary primarily for the control of built-up areas and illegal deforestation.
I also have a couple of my projects. One of which is the recommendation system of films. Therefore, it was interesting for me to work on a system of recommendations, as well as to dive deeper into NLP.
Position: Data Scientist
Category: Python
Experience: 0 years | English: upper

My achievements are mostly related to finance field and some competitions with challenge your logic and basic knowledge.
I do not have any programming related experience. But I have participated in virtual internship in data analytics in KPMG (Australia). In that program I have given a raw dataset about bicycle company. Firstly, I cleaned dataset, remove some unnecessary infos and make ready for analysis. Then by applying some calculations I determined target market for bicycle company and make some recommendations to improve sales of company.
Although I dont have any experience, I have a great analytical thinking. I think fast and make observations about data and I can analyse data by using tools such as python, excel and so on.
Position: Data Scientist
Category: Data Science
Experience: 0.5 years | English: upper

Certificates from CISCO.
IT essentials – a course from CISCO to increase the level of IT Essentials, basic knowledge about the network, software, computer and their construction.
Networking essentials - a course from CISCO to increase the level of Networking Essentials, fundamental knowledge about the network communication, internet, connections, protocols.
Startup School. 
Course from Astana Hub about startups.
I do not have experiences yet, but I have projects that I did.
PROJECTS
Red Wine Quality – analysis, visualization, and machine learning
Using the red wine quality dataset, machine learning was done that determined which properties most affect the quality of wine (sklearn library), data visualization (matplotlib, seaborn libraries) and data cleaning (numpy, pandas libraries).
Top 10.000 popular movies – analysis, visualization
Using a dataset of 10,000 popular movies, visualizations were made (matplotlib, seaborn libraries) and data cleaning (numpy, pandas libraries) in order to reveal various kinds of information about films for the 20th, 21st centuries.


House price prediction – data preprocessing, visualization, feature engineering and machine learning(supervised)
Using dataset about the houses and their price in Bangalore, India, I did the progressive data preprocessing and visualization for further analysis by the help of the pandas, numpy and matplotlib libraries. The main purpose of this project is to predict the prices of the houses using machine learning, so I used 3 ML of supervised learning(regression): lasso, ridge and linear regression and I did grid search to use the best hyperparameters for ML using sklearn library.

Heart disease prediction – data preprocessing, visualization, feature engineering and machine learning(unsupervised)
Using dataset about habits and the medical characteristics of the person, I needed to predict does he have heart disease or no. As my previous project, I did the same things, but after data preprocessing, I choose the best technique to deal with imbalanced data using imblearn library. According to the purpose of the project, I used 3 ML of unsupervised learning(clustering) – decision tree, random forest and KNN and I did grid search to use the best hyperparameters for ML using sklearn library.
Position: Data Scientist
Category: Data Science
Experience: 1.5 years | English: fluent

Taras Shevchenko National University of Kyiv:
* Master's degree, Mathematics, 2021-2023, GPA: 5.0.
* Bachelor's degree, Statistics, 2017-2021, GPA: 4.7.
I don't consider NLP and CV roles.

Data Scientist in Risk Modeling:
* Developed credit risk models using statistics and machine learning techniques;
* Contributed to enhanced decision making processes;
* Utilized Python, R and SQL for developing Application and Behavioral models;
* Conducted efficient T-SQL queries and feature engineering on large databases;
* Automated the creation of monitoring reports.

Machine Learning Intern:
* Performed marketing analytics on fridge sales data;
* Focused on demand elasticity estimation, product clustering, and time series forecasting;
* Leveraged Python for data analysis and modeling.