lang-uk/recruitment-dataset-candidate-profiles-english huggingface dataset:
Position: Data Scientist / ML Engineer
Category: Data Science
Experience: 3 years | English: intermediate

Silver Medal in NLP Kaggle Competition: Jigsaw Unintended Bias in Toxicity Classification
Main:
- churn prediction
- anomaly detection
- customer matching
- emails classification
- customer clusterization
- invoice paying forecasting
- forecasting sales of goods
- warehouse delivery planning
- text and table detection and recognition
- ticket sales forecasting and optimized price recommendation

Additionaly:
- metric learning
- distribution learning
- image generation (GAN, VAE)
- reinforcment learning (base approaches)
- face and person real-time recognition and identification.
Position: Data Scientist / ML Engineer
Category: Data Science
Experience: 2 years | English: upper


SKILLS AND KNOWLEDGE:
•	R: data.table, dplyr, lubridate, stringr, parallel, packrat, ggplot2, plotly, R Markdown
•	Python: pandas, sklearn, matplotlib, seaborn
•	Machine Learning and analytics: EDA, feature engineering, classification, regression, clustering, time series analysis and forecasting, statistical learning, hypothesis testing
•	Databases: PostgreSQL, Exasol
•	Developer tools: RStudio, Jupyter Notebook, PyCharm, GIT, bash

EXPERIENCE:
- solving classification problems on the data with hierarchical structure
- application of NLP techniques for knowledge extraction from textual data 
- R scripts development for data pipelines support
- support and improvement of demand forecasting models
- development and integration of automatic QC reports for modelling results
- optimization of models’ runtime (identification of memory leaks, code performance profiling)
- communication with client on a regular basis with regards to results delivery and alignment on future steps
- conduct knowledge sharing sessions for teammates
Position: Data Scientist / ML Engineer
Category: Data Science
Experience: 3 years | English: pre

Creation of a production version for visual search in 2 months. Created a production version for Camera Search in less than 1 year, working as one data scientist on the team.
-Worked with scientific projects, where use image processing techniques (denoising, image restoring, image distortion). - Worked with TF Lite, use C++ and Java, also use Tensorflow js for deploying neural networks. - Using WASM for implementation image processing algorithms. - In this time working on Visual Search project, which using for search similar products, use only image. - I am deploying neural networks on aws instances in Docker. - Creating the dataset and images processing system. This system using for creating dataset for our tasks. - Lead for a small Data Science team.
Position: Data Scientist | ML Engineer
Category: Data Science
Experience: 4 years | English: intermediate

My latest roles:

- **Delivery Manager / Data Scientist**. Since 2010, leading a team of engineers to build business automation software for large retail customers. I own a full cycle from product discovery and customer development to defining product specs, milestones, acceptance, post-sale delivery, integration, and support. Given my passion I also work as a hands-on Data Scientist for my team’s projects. I’m responsible for my business unit’s revenue overall, which I doubled in the past five years.

- **Founder, Data Analyst & Engineer**. I'm helping small retail businesses manage inventory with my product, used by over 100 retail micro spots across the country. It’s an Android barcode scanner app with a server-side back-end and advanced reporting (JS, Python, PostgreSQL). Beyond all the business development, I designed the product, defined specs, hired and managed freelancers to iteratively build the software. Worked as a hands-on engineer to develop the server-side admin modules (Django) that include data analytics (SQL, Pandas, Seaborn) for our customers.

**My strengths:** Soft skills, ownership, leadership, breadth of knowledge, and end-to-end software product development experience – from conception to post-sale integration and customer service.

**Area of improvement:** English – currently Intermediate, working with my personal coach several times a week on an aggressive learning path.
**I have over 15 YOE taking leadership roles in software & hardware development.** In the past 4 years, I became passionate about the future of AI, so I started getting back to IC work, refreshing my tech skills, and taking on projects as a hands-on Data Scientist / Machine Learning Engineer.

** Projects** – a few recent examples:

- **NLP**: An emotion detection system that tracks customer support quality within chat conversations and social media. Lots of work with the training data and fine-tuning RoBERTa for our NLP task.

- **CV**: A computer vision system for food object detection and classification. As applied to sorting and quality control within large food manufactories in Ukraine. TensorFlow, OpenCV, YOLO for object detection, video capture, etc.

I used my approach from both projects on public datasets and scored #1 in accuracy on Kaggle in each of them (inquire me to get two links in my resume).
I’m interested in DS roles at international companies that have a potential for me to scale into a Data Science Manager in the future.
Position: Data Scientist | ML Engineer
Category: Data Science
Experience: 3 years | English: upper

- Achieved 92% accuracy for logo object detection problem using Yolor.
- Developed model with 90% accuracy for sentiment analysis problem using TF-IDF and Logistic regression.
- Achieved 93% accuracy for car color detection problem using yolo, wrapped it into Fast API and docker.
- Made speaker identification model with 95% accuracy. 
- Increased accuracy of speaker diarisation model by 30%
- Set up Ambari HDP cluster with connected PowerBi dashboard.
- Made GCP infrastructure that increased neural network training speed and workflow 10 times.
Worked since Sep 2020 to Feb 2023 at Go Wombat Team at Data Scientist position.

During this period I worked at Silverbullet company's project at outstaff basis. It was aimed to build contextual targeting, brand safety and insights platform. I contributed to sentiment analysis and face detection models. Built logo detection model using state of the art approaches. Also worked on dataset creation and annotation via AWS MTurk. The last field of work was creating understandable and informative dashboard for existing models demonstration.
My second project was to build a car color detection model that could be easily ported to C++ and run on the road cameras.

Worked since Apr 2019 to Jan 2020 at AM-BITC LLC at Platform Engineer position.

During this period of work I worked on several projects. The first and main project was "Speaker Identification by Voice". Working with a colleague we achieved 95% accuracy. Also at this project I made back- and frontend for our Flask application. Next project that I done was cloud based analytics in PowerBi made in Microsoft Azure. After that I studied and completed Cisco CCNA course. Also it was time, when I deployed Tableau, Tibco and PowerBi servers.

Worked since Feb to Mar 2019 at Technorely Inc. at Junior ML Engineer position.

During this period I worked on Speech Recognition project. My task was to solve "speaker diarisation" problem. I took two most perspective existing models and improved it. First was written on tensorflow, second on PyTorch. During improving I used clustering methods (Spectral, K-means and others), wrote metric for evaluating, tried several combinations of architecture and dived into sound processing domain scope.

Worked since Aug to Nov 2018 at uData.tech at Technical Specialist position.

During this period I worked in DS team on three projects. On Financial project we were trying to answer the question "Disposition effect, good or bad?". I worked with pandas, matplotlib, seaborn Python libraries. On medicine project we were predicting the next disease of the patient via recommendation system, decision rules, RNN and LSTM models. I worked on 3-node Hadoop Cluster on PySpark visualizing and cleaning data. My last project was at agro sector. The main purpose was to build BI system and make price forecast. I scraped data, made database and Tableau dashboard.
Seeking interesting projects that need my skills.
Position: Data Scientist, ML Engineer
Category: Data Science
Experience: 11 years | English: intermediate


Data science, computer vision, drone development, Python-pandas-tensorfow-pytorch, OpenCV, SQL, Linux, elementary data engineering, former - a lot of variable development and system engineering tasks.
Non-formal environment, complex tasks.
Position: Data Scientist, ML Engineer
Category: Data Science
Experience: 3 years | English: fluent


Developed a back-end for web-application, which was used for neural style transfer, where you can  blend content image and style reference image (as a rule painting of famous artists) 

Transformed raw data with custom-made ETL application to prepare data for machine learning, using Pandas, Numpy, spaCy

Researched Auto- ML API features for NLP (from Google Cloud ML, Azure ML, Amazon Comprehend, IBM Watson) to evaluate a baseline for the optimization of our models, built from scratch.

Analyzed Sales data for Forecast Engine to find hidden patterns, detecting trends in sales over the years.
Position: Data Scientist, ML Engineer
Category: Python
Experience: 0 years | English: intermediate

I also worked as a teacher at school, which shows that I am stress-resistant and ready to take on any job.
I don’t have much experience working in a company, but I have pretty good experience working in a team. Because at the university I had to complete collective projects. There are still a couple of pet projects on my list. These include my laboratory work at the university.
I am ready to work with any projects and customers
Position: Data Scientist, ML Engineer
Category: Data Science
Experience: 1.5 years | English: fluent


I have experience in 
- data analysis and cleaning, 
- development of cloud-based data processing pipelines, 
- development of machine learning models.

Looking for the opportunity to level up my skills in production and research settings, with particular focus on NLP and Computer Vision. Especially interested in working on education, healthcare or other socially conscious projects.
Position: Data Scientist, ML Engineer
Category: Data Science
Experience: 1 years | English: fluent

Created a model for a mobile application based on InceptionV3 to recognize the mask on a person, which worked with an accuracy of 99.82%. Also created a unique method for parallel statistical estimates for coursework.
In my sophomore year, I worked as a programmer, but my responsibilities were in arranging labels for a computer vision program. I also did an internship at Samsung, where I created a mobile application based on Transfer Learning, which detects whether a person in a mask or not. Wrote a term paper on "Parallel Statistical Estimation", where I used my own unique method of estimating split data over a neural network. I want to develop in the areas of machine learning and statistics.
I plan to work in the field of analytics and machine learning. Ready to take an internship.
Position: Data Scientist, ML Engineer
Category: Data Science
Experience: 5 years | English: upper


Machine learning Tech Lead at Search department
Headed a group of two Data Scientists and a Python developer.
Led the development of a search project for goods and services. Besides the full text search engine this
project involved personal recommendations, goods classification and query slot-filling.

Senior machine learning engineer at Big Data department
Developed a system for crawl and extraction structured information about people, products and orga-
nizations from the web. This system reduced the cost of manually checking the loan application forms.
It also were used for providing data about special offers to our search engine.
Built a machine learning platform, which allowed analysts to rapidly deploy their models in production.


Freelancer at upwork.com
Developed a service which assists HR with compiling of vacancy description by providing auto-
completion for frequent domain-specific phrases. Built a model for domain-specific relevant phrases
extraction in English and Dutch languages. Created a high-performance REST API service with Scala.

Software developer at Dotin LLC
Platform architecture developing
Developed A social profile matching platform that is used to extract and process information about users
of social networks. Analyzed open linked data sources, such as dbpedia.org, to enrich user data.


Programmer, department of information retrieval
C++ \Python programming
Developed a task distribution system. Optimized algorithms for processing large data.
I am working on text search relevance and NLP related projects. Interested in application of one-shot and transfer learning in NLP.
Position: Data Scientist, ML Engineer
Category: Data Science
Experience: 5 years | English: fluent

My biggest professional achievement is: creating defect detection system based on deep learning approaches and deploy it on factory. I worked on this project for 9 month.
What i've done at previous work:
- developed BI-system based on manufacturer’s goods data;
- built ML models to solve customer churn prediction problem for gas station area;
- built prototype model for food detection problem;
- built models to solve surface defect detection problem ;
- created surface defect detection system and deployed it on factory;
- developed PoC solution that determines most similar outfits from database to given one;
- developed solution that distinguishes different body parts of human, deployed to ios devices using CoreML framework;
- wrote end-to-end roadmaps to the projects;
- performed managing tasks: managed processes inside team, communication with clients on technical issues;

I want to be a part of Data Science / Machine Learning strong team and work on difficult problems both based on tabular data and images/text/sound...
My main expectations from future company are: professional growth, interesting projects and competitive salary.
Position: Data Scientist, MLEngineer
Category: Data Science
Experience: 1 years | English: intermediate


Building and optimizing machine learning models.
Establishing and optimizing Deep learning models such as CNN, RNN, LSTM
Segmenting customers with algorithms such as RFM, K-MEANS, CLTV
Data analysis using statistical tests
Position: Data Scientist/ ML Engineer
Category: Data Science
Experience: 2 years | English: fluent


Creating end-to-end NLP solutions for document processing.

Worked on industry leading iGaming products (Compliance, geolocation, fraud prevention domains). 

My BSc thesis features image deraining solution based on RES-UNet architecture and transfer learning. Working now on a MSc thesis, that provides a little different approach on music generation.

Achieved accuracy equal to the winner of 'IEEE-CIS Fraud Detection' Kaggle competition back in 2019.

Worked as a programming teacher.
Position: Data Scientist/ ML Engineer
Category: Data Science
Experience: 1.5 years | English: fluent

I am the Amazon Web Services Machine Learning Scholarship Recipient 2021. 
I recently earned TensorFlow Developer Professional Certificate. 

I am also completing Google Machine Learning Engineer Professional Certificate Program.
Passionate about Machine Learning, Data Science and Statistics. Vast interest in Computer Vision, Natural Language Processing, Prediction and Classification Problems.

Data Science Freelancer.
Position: Data Scientist/ML Engineer
Category: Data Science
Experience: 0.5 years | English: upper

As a result of work on the current project, an effective method of classifying electroencephalogram signals has been developed.

Have 2 publications in the field of number theory. The papers were published in esteemed journal affiliated with my institution. 

“STATISTICAL AND ALGEBRAIC PROPERTIES OF THE DISTRIBUTION
OF PRIMITIVE ROOTS IN SETS OF CONSECUTIVE PRIMES”, No 36 (112)
(2022): Electrotechnic and Computer Systems

“SYSTEMATIC ANALYSIS OF THE DISTRIBUTION OF PRIMITIVE ROOTS
OF PRIME NUMBERS IN ARTIN’S CONJECTURE ”, No 36 (112) (2022):
Electrotechnic and Computer Systems

And the publication related to the project I'm currently working on.

“EEG Emotion Classification Using 3-Dimensional Convolutional Neural Networks”, viXra:2306.0037

Acquired professional DeepLearning.AI TensorFlow Developer certification and in preparation for the TensorFlow Developer exam.
From 8/2022 to 6/2023:
Conducted in-depth research on the statistical and algebraic properties of the distribution of primitive roots in sets of consecutive primes.
Collected and analyzed extensive data sets related to prime numbers, including calculating primitive roots and examining their distribution patterns.
Assisted in the preparation and revision of research papers, including detailed analysis, interpretation of results, and discussion of theoretical implications.
Utilized statistical analysis techniques to evaluate the observed patterns and assess the validity of Artin's Conjecture.
Utilized capabilities of Pandas, Numpy, Matplotlib and Numba for efficient calculations which led to interesting results.

Currently, I am involved in an ambitious project that focused on emotion classification based on EEG data. This project aims to harness the power of EEG technology to develop innovative methods for accurately classifying emotions, paving the way for new insights in cognitive science and human-computer interaction.

We proposed a methodology for classifying EEG signals based on signal processing using wavelet transform and superlet transform. The wavelet transform is used to decompose the EEG signal into frequency components, which are then used as features for classification. The proposed approach is evaluated using the publicly available “GAMEEMO” EEG dataset, which has been annotated by valence and emotional arousal.

Theoretical part can be found in the extended abstract:
 “EEG Emotion Classification Using 3-Dimensional Convolutional Neural Networks”, viXra:2306.0037
Position: Data Scientist/ML Engineer
Category: Data Science
Experience: 0.5 years | English: upper

Projects:
Steel defect detection (cv2, keras, pytorch)

Forest fires influence analysis (data mining and analysis, time series processing and analysis)

Character recognition using PCA (cv2, linear algebra)

Reinforcement learning course by SoftServe
I am a 4-year Computer Science student at Ukrainian Catholic University.  Here I`ve developed strong, soft skills as I was usually working in a team. I`m communicative, attentive to details, hardworking. I have tried myself in such fields of Computer Science: cybersecurity, web development (both front and back end), embedded, and now I`m hardly developing myself in AI sphere.

Working experience:
7 month as Python Engineer
3 month as ML Engineer

I had experience in such AI fields as:
- supervised/unsupervised learning: linear and logistic regression, SVM, decision trees, classifiers

- Deep learning: image classification and segmentation (keras & pytorch)

- Reinforcement learning: basics of DQN, Policy gradients, A2C, MCTS

- Time series forecasting: ARIMA models, differencing and decomposing of ts, predicting future profit

- Computer vision: image stitching, recognition, classification and segmentation

- NLP: authorship detection, sentence similarity, toxicity classification

On my last job, I was creating a recommendation system. I was responsible for data processing and visualization, feature engineering using statistical methods and some state-of-the-art techniques as TF feature columns. Also,  I was involved in the deployment of the model using TF Extended.
I'm expecting precise requirements and rules, friendly team, which are in passion of what are doing.
Position: Data Scientist/ML Engineer
Category: Data Science
Experience: 0 years | English: fluent


I am a Computer Science student at UCU with great organization and teamwork skills. I am eager to learn as much as possible in Machine Learning and Data Science and work on real-life projects to apply my skills.


As a part of my internship as a Python Developer, I created a RESTful API for a hotel management system. I employed the FastAPI web framework, PostgreSQL database, and the Alembic database migration tool to complete the project. This was a great experience in terms of working under mentorship and learning new technologies at a quick pace.


During my time in university, I have completed (individually and as a team member) several projects involving various domains:
Airbus Ship Detection (Image Segmentation)
-  Created an EDA notebook for the dataset with RLE encodings of the masks
- Designed a deep learning architecture utilizing a U-Net-like convolutional neural network and dice score for evaluation using TensorFlow and Keras

Automatic Ping-Pong Scorekeeper 
    - Trained a machine learning model using Edge Impulse to recognize the ball hits in real-time and participated in designing and developing a scorekeeper using C++

Coffee Trade Dataset Statistical Analysis 
    - Utilized R programming language to analyze a dataset of coffee production, export, and consumption and generated comprehensive reports and visualizations of the findings; published it on Kaggle

Monophonic Audio Transcription Using FFT and Goertzel
    - Created a command line application for monophonic audio transcription using FFT, DFT, and Goertzel algorithm; compiled a thorough comparison of algorithms’ speed and accuracy into a 10-page report in LateX

Music Player using Spotify API
    - Worked in a team of 4 people to create a music streaming service using Spotify API with a technology stack of ReactJS, Express.js, and MongoDB
In terms of my professional development, my goal is to enhance my skills in the field of Machine Learning/Deep Learning and Data Science. I am eager to become a specialist in those areas and want to contribute to complex and interesting projects.
Position: Data Scientist/ML Engineer
Category: Data Science
Experience: 1.5 years | English: upper

I have successfully completed some courses related to statistics, programming and ML (coursera, stepic, prometheus). I graduated from university where I was interested in space imagery so I have had a few projects on this subject.
ML Engineer/ 3 months
Projects: Audience outflow analysis, Object detection, Face recognition, etc. 

Data Scientist/ 1 year
Projects: Instance Segmentation, OCR, Gene analysis, Object Detection, Anomaly Detection, Paraphrasing text, Web Scraping, etc.

Social Network Researcher/ 10 months
Tasks: working with various social networks and finding the necessary information in them, creating
datasets, data labeling, using XPath requests for receiving information, data preprocessing.
I want to work in a friendly and motivated team, have interesting tasks (CV, NLP), develop and grow professionally.
Position: Data Scientist/ML Engineer
Category: Data Science
Experience: 0.5 years | English: upper

Solid understanding of machine learning/deep learning fundamentals
(pros & cons of different model types, validation, metrics, etc.);
• Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras,
NLTK;
• Familiar with: AWS (SageMaker), MLflow, C/C++, Java, Docker, Linux,
Git, MySQL, MongoDB;
• IDE: PyCharm, Jupyter Notebook, MS Visual Studio, Eclipse, Intellij Idea;
Work Experience:
• 2018 – 2019 - Experience in business correspondence with foreign
clients;
• 2020 – 2021 - ML/DL researcher at the Computer Engineering Faculty.
Projects:
• Malware detection and classification using LDA;
• ML research for the classification of cardiovascular diseases;
• Pneumonia X-Ray detection system;
• Food Vision app;
• Time series prediction of the stock market;
• SMS Spam detection.