lang-uk/recruitment-dataset-candidate-profiles-english huggingface dataset:
Position: Data Scientist
Category: Data Science
Experience: 10 years | English: fluent


Aug.2020 - present
Machine Learning Researcher - FUSE AI, Hamburg
Computer Vision on MRI data  

Sep. 2016 – Aug. 2019 
Postdoctoral Researcher: Member of the SHiP collaboration, CERN, for Humboldt Universität zu Berlin, Berlin, DE
Research Area: 
    • Analysis of the simulated SHiP Data. Vertex reconstruction of the neutral particles which decay to two charged particles and for which the tracks can be reconstructed in the SHiP Spectrometer.
    • Software implementation of the geometry of the SBT Detector in Geant4-based simulation of the SHiP experiment
    • R&D of “Surround Background Tagger” (SBT) part of the ShiP detector project at SPS ring, CERN
    • Developing Software for signal readout with a digital oscilloscope from the SiPM-/Photomultiplier
    • Experimental Design of Beam Test at CERN of the SBT detector prototypes
    • Leading data analysis group
    • Supervising students
      
Apr. 2016 – May 2016
Postdoctoral Researcher: Member of the CMS collaboration, CERN, for DESY, Hamburg, DE
Research Area: 
    • Analysis of the data from the CMS experiment at CERN

Sep. 2012 – Apr. 2016
Scientific staff / Doctoral student: Member of the CMS collaboration, CERN, for DESY, Hamburg, DE
Research Area: 
    • Analysis of the data from the CMS experiment at CERN: Measurement of the double differential top quark pair production cross section in the dilepton mode
    • R&D of CMS Pixel detector prototypes for the Upgrade project of the CMS Detector at CERN
      
Nov. 2010 – Sep. 2012 
Guest Scientist: Member of the ZEUS collaboration at DESY, Hamburg, DE 
Research Area: 
    • Analysis of the data from ZEUS experiment at HERA collider, DESY
Position: Data Scientist
Category: Data Science
Experience: 3 years | English: fluent


Automated business processes by enabling cutting-edge technologies and trading
off with acceptable depth of explainability. Built a fraud detection model disclosing fraudulent claims, outperformed baseline by 30% at MVP stage. As a part of optimization team significantly reduced claim handling costs of the leading insurance company of the country. Lead a team of engineers building CV model for car damages detection and assessment. Developed custom OCR engine to accelerate OPS steps, gradually reduced growing FTE rate and boilerplate work. Closely engaged in each block of the pipeline of AI projects. Conduct several EDA's revealing factors inhibiting business grow. Participated in core ERP systems development by designing data models to govern business pocessess properly. Set up company's reusable source code library by adding most robust and production proven blocks of code. Max focus on value the tech can deliver to business
Position: Data Scientist
Category: Data Science
Experience: 2 years | English: pre

Automated daily scripts with Apache Airflow. Built live dashboards for main business metrics. Developed and implemented content-based and collaborative recommendation systems. Reduced monitoring of indicators from T-1 to real-time. Analyzed NLP algorithms and built a search engine based on them. Built computer vision models for face recognition and fraud detection
Automated daily scripts with Apache Airflow. Built live dashboards for main business metrics. Developed and implemented content-based and collaborative recommendation systems. Reduced monitoring of indicators from T-1 to real-time. Analyzed NLP algorithms and built a search engine based on them. Built computer vision models for face recognition and fraud detection
I want to increase my machine learning - deep learning skills.
Position: Data Scientist
Category: Data Science
Experience: 1.5 years | English: fluent


A year of experience in Data Science. Worked with ML and DL frameworks along with classic DS toolkit.

Commercial experience:
* ReLive Intelligence
Data Scientist |  June, 2022 - Nov, 2022
- Developed, tested and maintained Neural Networks that could detect rail cars with OCR for license plates using Python and PyTorch to automate train tracking and identification on railways.

- Developed data augmentation algorithm for object detection from scratch using Calculus and Linear Algebra that increased dataset size from 10.000 images to 150.000 and optimized its performance by 400% using multiple threads for computations.

- Conducted cloud computations using AWS Sagemaker and AWS S3 for data storage

- Collaborated with DevOps and Software Engineers for model deployment on mobile devices and surveillance cameras.

* Astana IT University
Research Assistant | March, 2022 - October, 2022
- Translated MATLAB code to Python and implemented differential equations using SciPy module
- Developed interactive dashboard for data visualization using IPyWidgets module.

* Astana Hub
Data Analyst | June, 2021 - August, 2021
- Conducted data analysis using Yandex.Metrics and Google Analytics to retrieve data which resulted in a 50% increase in website loading speed.


Capstone projects:
* Console bank application, 2021
Developed bank application as a final project for university OOP course that could save and retrieve data from database as well as perform transactions using Java and PostgreSQL.

Pet projects:

* Heart Disease Analysis:
- Built a Machine Learning model that predicted chance of the heart disease of patient depending on his overall conditions and health-related features. Accuracy of model is 93%
I am a passionate Data Scientist with great interest in data related tasks, seeking for opportunities to challenge my development, problem-solving and analytical abilities in order to expand my knowledge and potential.
Position: Data Scientist
Category: Data Science
Experience: 0.5 years | English: intermediate


Bachelor's thesis on machine learning
Writing a bachelor's thesis on machine learning using data analysis on Python
Various python libraries are used, such as Pandas, Matplotlib, Numpy, Sklearn
Built and trained various machine learning models based on different methods to predict
the use of electricity in industry
Position: Data Scientist
Category: Data Science
Experience: 3 years | English: fluent


Background:
~ (2022 - present) - Data Scientist at MarineTech. Building customized deep neural net architectures to help facilitate scientific progress in maritime research.
~ (2020-2022) - Data Scientist at Mind&Machine, Moscow, Russia. Building and fine-tuning machine-learning algorithms to optimize retail processes. Data mining, data analysis.
~ (2019) - Internship at SeeTree, Tel-Aviv, Israel. Studying correlations in geospatial data related to the "well-being" of orange trees. Building sun-tracking modules, to add radiation hours to the tree data.
~ (2019-2020) - Udacity's Data Science & Machine Learning Engineer Nanodegree;
~ (2015-2019) - Bachelor of applied mathematics and statistics at the Erasmus University Rotterdam, Netherlands;
Position: Data Scientist
Category: Data Science
Experience: 2 years | English: intermediate


Background
General: Mathematical Statistics, Probability Theory, Python
Time-Series Analysis: Time-Series Classification, change points detection
Intellectual Data Analysis: Statistical data analysis, data processing, detection of multi-correlation
and heteroscedastic effects, missing data handling, outlier detection, clustering algorithms
Machine Learning: Applying metrics for understanding the quality of ML models, overfitting detection techniques, imbalance data handling, feature importance

Skills
Programming Languages: Python, R, SQL
Web Technologies: HTML, CSS, JavaScript
Frameworks: Flask, Django
Version control: git, GitHub/Bitbucket
CI/CD: CircleCI, Jenkins
Databases: MySQL, PostgreSQL, MongoDB, BigQuery
Cloud providers: Google Cloud Platform
IDE’s: PyCharm, VSCode, Jupyter, Eclipse 
Testing Tools: pytests, unittests, Postman 
SDLC: Agile, Scrum, Kanban, XP
Operating Systems: Windows, Linux
Others: Jira, JSON, XML, CSV, REST, pandas, numpy, keras, scikit-learn, matplotlib, seaborn, scipy,
  spacy, nltk, TextBlob, wordcloud, lightgbm, XGBoost, catboost, pydantic, alembic

Work experience:
- Python developer - IGTM NAS of Ukraine
June 2020 – August 2022
- Middle data scientist in ATB-MARKET
March 2023 - present time

Education
Bachelor degree. 2017-2021. System Analysis. National Technical University. 
Master degree. 2021 - 2022. System Analysis. National Technical University.
Position: Data Scientist
Category: Data Science
Experience: 4 years | English: upper

Leading project based on highly complex business logic.

Was invited as a mathematician to the project to develop in a short term and present the solution to clients.

Together with team achieved TOP 14% in AMEX Kaggle competition.

Was a college lecturer for 1 term. Probability and statistics.
BA in Mathematics
Master degree in Mathematics
(Stochastic differential equations area)
Master degree in Financial Economics / Applied economics
PhD in Mathematics (currently) (Stochastic differential equations area)

Data scientist:
-	Building a recommendation system
        (Collaborative filtering,  LSTM)
-	Making research on behavioral biases’ impact on traders’ performance
-	Doing analytics with parsed data from Facebook
-	Finding the best traffic routes with dynamical visualization
-	Finance project:
        Creating technical indicators, Wavelet/Fourier transformation for features extraction, Cluster analysis, PCA, Autoencoder network for dimension reduction, t-SNE for visualization

Data Analyst / Engineer
-	Building Data Analytics ETL pipeline in Databricks with PySpark (+unittest, pyodbc)
-	Sales Forecasting using ARIMA model
-	Rewriting MSSQL queries to PySpark
-	Developing Machine Learning approach

Machine Learning Engineer / Data Scientist
-       Time Series forecasting using FFT, Facebook Prophet model
-       Developing method for probability calculation in clinical trials which is based on Diophantine inequality, Monte Carlo method for multiple integral derivation
-       Binary imbalanced classification, anomaly detection based on complex business logic in financial market. AWS Sagemaker, sklearn processing jobs. Leading Junior Data Scientist.


College lecturer (1 term): Probability theory and statistics
Data Science, Predictive analytics, Quantitative Finance, Time Series Forecasting, Signal Processing, Mathematical modeling, Gambling, Portfolio optimization
Position: Data Scientist
Category: Data Science
Experience: 1 years | English: upper

I took 2nd place in the Hackathon for the marketing derpartment in SkillFactory LLC

Based on given data I found the ways/methods to increase the quantity of new visitors  up to 10K per month for the client's website

Know English, Chinese, Russian
Bank scoring.
	Data analysis
	Finding insights and adding them to the origin data set
	Building machine learning models

I applied sveral conventional ML algorithms such as XGboost, LightGBM and RandomForest to achieve best ROC-AUC metrics.

YouTube Collaboration Hackathon
	Data set engineering
	Data analysis
	Verification of the hypothesis via statistical libraries in python
Position: Data Scientist
Category: Data Science
Experience: 3 years | English: upper

Hackathon INT20H 2020 — The I place in the Data Science category
Being a Data Scientist, I have deep knowledge in machine learning, statistics, math, and related fields. My passion is developing machine learning solutions with high efficiency and great accuracy. I have a strong knowledge of Python 3 and good knowledge of backend technologies. I do not spend my time in vain and try to be as productive as I can.
Looking for a job with a flexible schedule and professional grow.
Position: Data Scientist
Category: Data Science
Experience: 0 years | English: upper

I am a talented and highly motivated hardworking third-year student of Lviv Ivan Franko National University, Faculty of Mechanics and Mathematics, “Data Analysis and Mathematical Modelling”. I am passionate about acquiring new knowledge, especially in Mathematics, and Data Science. I can learn quickly and effectively apply new knowledge. I have a solid knowledge of Mathematics (Mathematical Analysis, Linear Algebra, Statictics, Probability theory, Number Theory etc.). My mathematical abilities provide me with strong analytic skills, problem solving ability and attention to detail. My studies at the university are highly successful as a result I took first place in ranking among second and third-year students. I 
 have tried various areas of programming, in particular, studied HTML, CSS, JavaScript, Python, SQL and some other technologies. Now I am focused on learning Data Analysis and Machine Learning, using Python and R programming languages. I have already taken several Data Science and Data Analysis courses. Also, I have strong knowledge of English at the B2 level.
Being still a student I don't have specific working experience, but I am always passionate to acquire new knowledge, therefore I have already finished several Data Science and Data Analysis courses.
Being motivated in learning data science I have started taking the set of courses Applied Data Science with Python by the University of Michigan on Coursera and have already finished two courses. 
In order to implement my knowledge I did a basic churn analysis project, in which I used and compared different classification models.
Position: Data Scientist
Category: Data Science
Experience: 6 years | English: fluent

1) I have created a speech to text model in half a year for Azerbaijani and now it is the most accurate model. It gets 10.8% WER, where our opponent gets 28.9% WER and Google gets 56.9% WER.

2) I have built the first automatic media monitoring system for Azerbaijani, where I have applied, Named Entity Recognition, Sentiment Analysis, Text Classification, Keyword Extraction and Summarization.
- Best Azerbaijani speech to text model:
I have built the speech to text model for Azerbaijani language using dataset that we used crowdsourcing to gather.
Technologies used: kaldi, srilm, python, bash, pytorch, numpy, pandas

-  Azerbaijani text to speech model:
I have built a text to speech model for Azerbaijani language using relatively clean utterances of two speakers. I used tacotron 2 and parallel wavegan models.
Tools used: python, pytorch, numpy, pandas,

- Media Monitoring project:
Wrote 200 crawlers to continuously grab news from 200 different news channels in Azerbaijan with a junior developer. Also applied generic crawlers to grab the news inside even if the html in the page changes.
Applied Similarity Detection on those news to get the unique news.
Applied Sentiment Analysis to classify those news as positive or negative.
Applied Text Classification to classify those news to different categories (Sport, Economics etc.)
Applied Named Entity Recognition to detect entities passing in the news to match them with the positive and negative news, so we can say that the name of this person or organisation is passing on negative news x% of the time.
 Tools used: python, scikit, numpy, tensorflow, keras

Similarity detection, Named Entity Recognition, Sentiment Analysis, Text Classification. Tools used: python, scikit, numpy
I want a place where I can continuously improve myself and work on different projects in Natural Language Processing, Speech Recognition, Computer Vision. Also I want a fair amount of payment.
Position: Data Scientist
Category: Data Science
Experience: 0 years | English: upper


Big Data Analyst
- Identification valuable data sources and automate collection
processes
- Preprocessing of structured and unstructured data
- Processing, cleansing & verifying of data
- Large amounts of information analysis to discover trends and patterns
- Building predictive models
- Building machine-learning algorithms
- Presenting information using data visualization techniques
- Visualizing results of analysis and creating interactive dashboards using PowerBI
Interested in executing analytical experiments to help solve different problems across various domains; analysind data for trends and patterns; implementing analytical models into production by collaborating with software developers and machine learning engineers.
Position: Data Scientist
Category: Data Science
Experience: 1 years | English: upper

Data Scientist with experience in statistical analysis and machine learning (scikit-learn, Pandas, NumPy). Background  in web development (full stack) with 10+ years of experience in relational database 
and web servers (CentOS with NGINX/Varnish/Apache/PHP/MySQL) management. Quality and results oriented. 
A team player who assumes responsibility and can work independently. Native Ukrainian speaker.
Bitcoin price prediction:
Tried to predict bitcoin stock price using its previous price time series.
Obtained data from Binance exchange for use in a time series prediction model.
Explored and enhanced the dataset through log and differencing transformations.
Created a hyperparameter-tuned models (ARIMA, LSTM) for bitcoin price through grid search.
Proved that forecasting the price of bitcoin stock can not be achieved using only its own previous price.  

Identifying pneumonia disease: 
Identified pneumonia disease on chest X-rays by image-based deep learning.
Downloaded images from Kaggle, explored and resampled data.
Built CNN models using grid search for hyperparameters tuning, including pretrained VGG19 models with data augmentation with 88% accuracy and 94% sensitivity.
Shows that this neural network has the potential to learn with a high degree of generalization.
Proved that the transfer learning approach can be used for the diagnosis of common human diseases.

Tanzanian water pumps status analysis: 
Tried to find ways to improve water pumps operation and consequently quality of life in Tanzania.
Collected water pumps data from Taarifa and the Tanzanian Ministry of Water.
Handling missing values in the dataset for continuous features with an iterative imputation strategy.
Modeled the data using strategies including Logistic Regression, Decision Tree, Random Forest, and XGBoost.
The random forest performed better than the ensemble method with accuracy 80,55% and 79.96% respectively.
Position: Data Scientist
Category: Data Science
Experience: 2.5 years | English: upper


Building and evaluating knowledge graph in a medical domain.
Would consider any option

Would also be particularly interested in transferring to the Computer Vision domain.
Position: Data Scientist
Category: Data Science
Experience: 5 years | English: upper

- have a publication in the NLP domain on EACL
- Won a few ML hackathons
- took part in a lot of different learning and community activities
Building a system that can score how likely leads/prospects will convert for the insurtech company to reduce costs and increase efficiency. Worked on:
 Preparing data
 Business understanding of data
 Preprocessing and generating features
 Training binary classification models
 Creating a prediction pipeline
 Analysing historical data about telemarketing calls and creating an algorithm to detect performance degradation for phone numbers.
Tools and technologies: CatBoost, LightGBM, XGBoost, FB Prophet, numpy, pandas, scikit-learn, scipy, SnowFlake, AWS SageMaker


Two parts of a project:
 Building a test results management tool
to filter false-positive errors and reducing
the cost of testing.
 Experimenting with an automatic tool
to detect, interact with, and check all the objects on the web page.
Worked on:
 Collected and processed raw data from the data warehouse.
 Generated features for analyzing different errors.
 Built and tested statistical hypotheses.
 Built and validated models.
 Generated the model's results reports.
 Built a pipeline for retraining a models
 Built an automated tool for Windows.
 Developed a logic for object detection
and interaction.
Tools and technologies: pandas, CatBoost, Keras, scikit-learn, OpenCV, win32auto, selenium, Flask, OracleDB.
1) Interesting and challenging project withreal world applications.
2) Motivated team that is constantly learning something new and developing its expertise.
Position: Data Scientist
Category: Data Science
Experience: 2.5 years | English: intermediate


Building machine learning models:
- clusterization (customers clusterization using kmeans)
- client activity forecasting
- churn prediction
- fraud detection
- sales forecasting using time series
- recommendation system
- data analysis, building reports with PowerBi
Looking for challenging projects and mentoring:)
Position: Data Scientist
Category: Data Science
Experience: 3 years | English: intermediate

Master's Degree in Computer Science
Building models for binary and text classification, regression, and recommendation problems (machine learning, deep learning, statistical methods).
Time series analysis and forecasting.
Models tuning and optimization. 
Getting data from the web.
Data preprocessing, and analysis. 
Feature engineering.
Position: Data Scientist
Category: Data Science
Experience: 2 years | English: intermediate


Building models for classification and regression problems(statistical methods, machine learning, deep learning). 
Recommender system (Collaborative and Content-based filtering).
Models tuning and optimization. 
Getting data from the web. 
Data preprocessing, and analysis.
Position: Data Scientist
Category: Data Science
Experience: 2 years | English: fluent


Build several Data Science projects in areas:
NLP, Computer Vision, Recommender System, Predictive Modeling, Time Series

Proficient with tools and algorithms:
Deep Neural Networks, CNN, RNN, LSTM, linear regression, logistic regression, clustering, PCA, visualization

Perform:
Data Collection - can scrap using beautifulsoup/selenium/scrapy
Data Cleaning - Pandas, NumPy 
Feature Engineering - take a deep look into the data and come up with new useful features
Data Preparation - can take raw data and transform it so it can be fed to an ML model/Neural Network
Modeling - build ML models and Neural Networks using sklearn, Tensorflow, and Keras
Data Visualization - can visualize the data and complex relationships using python libraries matplotlib and seaborn

Deployment:
Can deploy the model to the web using Flask or dockerize it using Docker.
Expect to work with various Deep Learning/ML techniques, architect and build complex models using Tensorflow. Want to preprocess the data and train a model on it, evaluate and then deploy. Visualize the outcome and model's performance to the business side.
Don't want to:
Translate code from one language to another; Work with BI; Write too many reports - I'm productive when I work, not when I write reports all the time.
Position: Data Scientist
Category: Data Science
Experience: 3 years | English: upper


Built an association rule-based recommender system
Built a custom object-detection model (Tensorflow) that extracts information with OCR (EasyOCR), which served as a microservice (FastAPI)
Built a text classification model that fixes misspelling input (NLP, fastText)
Built a data migration tool that allows getting data from various sources. Python, React, Flask, Selenium, and MongoDB are some used techs
Published a Python package for Database CI-CD automation (deploydb on PyPI)Built an association rule-based recommender system Built a custom object-detection model (Tensorflow) that extracts information with OCR (EasyOCR), which served as a microservice (FastAPI) Built a text classification model that fixes misspelling input (NLP, fastText) Built a data migration tool that allows getting data from various sources. Python, React, Flask, Selenium, and MongoDB are some used techs Published a Python package for Database CI-CD automation (deploydb on PyPI)

Skills: Recommender Systems · Optical Character Recognition (OCR) · Natural Language Processing (NLP) · Web Scraping · Python (Programming Language)