lang-uk/recruitment-dataset-candidate-profiles-english huggingface dataset:
Position: Machine Learning Engineer
Category: Data Science
Experience: 5 years | English: fluent


Projects and Tasks Completed:
I have had the privilege of working on a variety of projects over the past few years, encompassing a wide range of technologies and tasks:
1. Speech Recognition Project (2 years): This project was a comprehensive end-to-end endeavor. I began with data collection and analysis to understand the requirements and nuances of the task. I was involved in modeling, where I designed and implemented speech recognition models. Continuously, I tracked competitors and industry trends to ensure our project remained competitive. Model training and testing were pivotal aspects of my role, ensuring that our system performed optimally. For these parts I used SQL for data collection and queries, PyTorch and PyTorch-Lightning for trainings. I also dedicated significant effort to making the system noise-robust and accent-robust, enhancing its usability across diverse environments and accents.
2. NLP and text post-processing: In addition to speech recognition, I worked on various natural language processing (NLP) tasks. This included named entity recognition, word tagging, text summarization, and inverse text normalization. These tasks required a deep understanding of NLP techniques and tools, like LLMs, Bert, Language Modeling from scratch, etc.
3. Medical Audio Analysis (Thesis): During my Master's thesis at Technical University of Munich,  I conducted research in medical audio analysis. Specifically, I focused on generative technologies for audio data in a medical context. This work contributed to the broader field of healthcare technology.
4. Bio-Physiological Data and Cognitive Load Analysis: In my current project I'm working with bio-physiological data and I'm trying to model data and derive predictions from data provided by bio-sensors, such as stress, tiredness, heart rate, etc.
My current role in this team is being engaged in data analysis, problem formulation, modeling, coding and research tasks. I'm now working in a flat-surface team, which gives me both the freedom and responsibility of achieving my goals on my own, without being co-dependent of my colleagues, with that, of course, a team-driven environment is a huge concept of my work culture and I'm working with a team with a lot of excitement.

While I have gained valuable experience in various domains, I am always eager to continue learning and growing. Specifically, I am interested in latest advancements in ML and NLP and strengthening my leadership and management skills.
Position: Machine Learning Engineer
Category: Data Science
Experience: 4 years | English: upper

Educational Kaggle Competitions Prizewinner
Kharkiv IT Cluster Scholar
Competitive programming:
- Samsung UA Code League 2018 Finalist
- High Achievements in KPI Open 2019
- Nure Code Jam 2018 Winner
- Google Code Jam 2019/2020/2021
- Google Hash Code 2019/2020/2021
- ACM ICPC 2019 1/8, 1/4 and 1/2
- ACM ICPC 2020 1/8, 1/4 and 1/2
Projects:
- Platform for chatting with virtual characters
- Mobile app with real-time face filters
- Virtual advertisement placement in sport events
- High-quality image matting service
- Real-time people identification system

Areas of expertise:
- Computer Vision: image classification, segmentation, generation, face recognition
- Natural Language Processing: information retrieval, language modeling, text clustering

Responsibilities:
- Analysis of the subject area, the proposal of hypotheses for solving the problem
- Papers and team's ideas implementation
- Model training, quality assessment, diagnostics
- Data generation and filtering
I aim to work in ambiguous and cozy product companies that bring value to customers using the latest machine learning technologies. I like to perform challenging research and development tasks, which drives me to constantly learn something new. Also, I love to get knowledge and ideas from my team members as well as share my own with them.
Position: Machine Learning Engineer
Category: Data Science
Experience: 2 years | English: fluent


Projects:
- Pose estimation for Golf AI assist app
•Maintained and improved Pose Estimation solution for Golf players
•Worked with 2D/3D pose estimation models to improve metrics and biomechanical trackers
•Created and deployed Semi-Supervised Pipeline and Auto-Labeling tool for Data Team
•Managed deployment to GCP using Kubernetes Engine and Vertex AI

- Generative AI projects
•Developed several projects in Generative AI domain (Stable Diffusion, Dreamfusion)
•Managed deployment using following AWS services:
•Sagemaker
•S3
•EC2
•API Gateway
•Lambda
•Worked with several Serverless GPU providers
•Modal
•Runpod

- Dental AI smile analysis
•Developed dental module for AI smile analysis.
•Achieved 87% IoU for for teeth detection and segmentation using MaskRCNN
•Implemented YOLOv5 for teeth diseases detection.
•Leaded an R&D for several different modules using various Computer Vision techniques to plan and
implement requested features
Position: Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: fluent


Projects

Pulmonary fibrosis
Description: having unstructured list of patients’ visits info and 3d CT scans of their lungs, the goal was to predict the lungs’ capacity by the certain date and give the probability of the prediction. Tabular data was normalised and concatenated with the flatten output of the scan after putting it into efficientnetB6. The success metric was Laplacian.
Stack: efficientnet, EDA, sklearn, numpy, pandas, tensorflow, keras, matplotlib, seaborn, PIL.

G2Net signal detection.
Description: Waves from 3 different detectors (like LIGO) were given and the task was to identify, whether the signal existed among these or not - binary classification.
Stack: keras, sklearn, scipy (signal processing, CQT,CWT filters), math, pandas, numpy.

ReIdentification:
Description: Detecting and tracking people on the video in real time and identify them. Stack: yolov5, deepsort, pytorch, flask\dash.

Text generation:
Description: using trainable text networks to generate compliments for the certain field of the LinkedIn profile: languages, certifications, summary, experience etc.
Stack: t5, gpt2, spacy.

Lead message generator (Leadbot)
Description: having proposal on the upwork, task was to generate the relevant lead message to a customer. Message consisted field for filling by the text networks and templates given from the DB.
Stack: flask, gpt2, nltk.

Matching system
Description: the task was to match cv’s and job description according to the keywords, those were extracted and where. Paragraphs were categorised and splited by t5 network. Keywords were taken from sql tables and filtered in order to increase quality of info extraction. 
Stack: t5, sql, nltk, spacy, NER, OOP.

Text Classification
Description: the list of the transactions were inputs and their tax categories were output. Several approaches were tested: tf-idf classification, word embeddings, sentence embeddings. 
Stack: fastText, sklearn.

Formulas’ photos to latex code transformer
Description: Inputs were images and output should be the formulas as a sequence of latex tokens - Image to text generation. End-to-end networks were implemented, as Image transformers. The best decision was to mask and detect symbols separately, classify their BB’s and write rule-based system for transferring their coordinates and classes into sequence of latex tokens.
Stack: pytorch, tensorflow, U-net, SVM, huggingface.
Position: Machine Learning Engineer
Category: Python
Experience: 5 years | English: fluent

PhD in Math Imperial College London, UK. 
Specialist in Computer Science.
Great experience working in Europe
Proven Machine Learning Software Engineer with 5+ years of experience (banking, telecom, pricing) in delivering production-grade (serverless) automated python ML pipelines, refactoring code for production, splitting monoliths, data extraction.
I collaborate with data scientists, product managers and backend engineers, R&D teams in both big corps & startups. 
I am looking for projects with sensible business models in product startups involving data science, machine learning and data extraction, ETL orchestration.

Proficient in Python, SQL, Spark, Kubernetes, Airflow, Google Cloud,  AWS and strong background in both classic and deep learning algorithms.
I have a PhD in Machine Learning from Imperial College London and a Specialist degree in Computer Science,

I am currently working on cutting-edge projects utilising  Python(TensorFlow, LightGBM, HyperOpt) in a Kubernetes environment, and building robust ETL processes using  Scala code in Airflow, Kubeflow, VertexAI.
I am expecting to build, deploy and maintain ML models and supporting applications, gather requirements from data scientists so that I can design and implement the necessary software infrastructure to deploy and maintain the resulting ML models in production. In short, I can handle and be responsible for all aspects of machine learning project, from data collection  and preparation to model development, deployment and ongoing monitoring and maintenance
Position: Machine Learning Engineer
Category: Data Science
Experience: 0.5 years | English: upper


Python (3 y.), C++ (2 y.), TensorFlow (1 y.), Computer Vision (<1 y.), Speech Processing (<1 y.)
Do not prefer working with tabulated data (aggregated statistics, payrolls, large spreadsheets, etc.)
Prefer computer vision, deep learning.
Would be great to participate in research.
Position: Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: fluent


R&D Machine Learning Engineer with over 3 years of experience and strong academic background.

•	Implemented and evaluated machine learning algorithms for land cover classification using satellite data.
•	Built tools to automate data acquisition and preprocessing tasks.
•	Performed evaluation of prototype models.
•	Completed data cleaning and data validation.

Have experience in computer vision. Solved image classification and segmentation problems.
Position: Machine Learning Engineer
Category: Data Science
Experience: 2.5 years | English: upper


Research and development of a sound event detection system in a healthcare project: dataset collection and labeling, SED model design, training and evaluation, metrics design and experiment tracking. 
Development of a content recommendation system for an educational platform: defining of business objectives, data preprocessing and feature engineering, combining user-based and content-based recommendations, building model pipeline using PySpark, metrics and experiment tracking implementation.

Research, development and deployment different approaches for content watermarking using deep learning architectures such as encoder/decoder, GANs etc; designing loss functions for better model quality; metrics implementation and calculation.

Bot detection task in Instagram using pre collected client’s dataset: dataset markup using clasterization, data preprocessing and binary classification with tree-based algorithms and deployment on client’s server.
I'd like to work in a professional team with a lot of possibilities for growth either for me and for the project. I'm interested in different fields of signal processing, especially in sound processing, also i'd love to work if the field of recommendation systems development.
Position: Machine Learning Engineer
Category: Data Science
Experience: 4 years | English: fluent


Researched and developed ML applications for Natural Language Processing tasks in fintech.

- Implemented a news monitoring system using a multi-label linear classifier. This task implied working with taxonomies.
- Built a generic experimentation pipeline of unsupervised hierarchical multilevel multi-label classification for different text domains. As part of the project, I was working in collaboration with a knowledge engineering team on improving unsupervised keyword-driven classification techniques and implementing word sense disambiguation methods for improving keyword- based Unsupervised Text Classification. 
- Implemented a relevance filtering system. This task implied researching and exploration of pre- trained embeddings (GloVe, Spacy, Word2Vec, Universal Sentence Encoder, Transformer-based encoding) as well as embeddings to be trained (TF-IDF, RNN-based encoding).
Implemented a facts extraction system based on a question-answering transformer-based model. As part of a task, worked on the collection and annotation of domain-specific data.
Position: Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: intermediate

Developing iOS/Android applications from scratch with publishing to store (full application life-cycle).

Successful implementation of the machine learning systems in the mobile gaming/advertising fields.
Responsibilities:
- Designing and developing cross-platform utilities (in order to replace native Android/iOS tools)
- Implementing in C++ a SQLite ORM
- Patching of the physical engine (chipmunk 2d)
- Developing architecture of applications according to the principles of object oriented design
- Implementation of the in-game storage cloud, JSON-RPC services
- Developing regression analysis system for predictions of the financial data
- Implementation of the parses (JSON, XML)

Main skills: C++, STL, JNI, android, cocos2d-x, SQL.
Secondary skills: python, sklearn, numpy, pandas, keras
I'm obsessed about: machine learning, data science.
Position: Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: fluent


Result-driven expert with mature and realistic approach to work and technology. With proven background in delivering high quality projects related to process automations in AP\AR\GL\MDM\HR. 

Machine learning projects including but not limited to: - timeseries forecasting - anomaly detection - natural language processing (classification and clustering) - data science projects related to patiens classification I'm building my career on t-shaped approach. Where my main focus is python, machine learning but I'm also familiar with finance business, cloud and SAP. 

I’m not only focused on providing high quality project but also on very good communication with the business partners. I'm comfortable with wearing multiple hats, communicate complex topic to non-technical audiences and also translate their requirements into code. 

I reported to variety levels of technical and non-technical leaders. Technologies that I’m using are: - Python - SAP - VBA - SQL - Azure - Alteryx - Automation Anywhere \ UiPath Looking forward for certyfication in Azure Data\ML
Position: Machine Learning Engineer
Category: Python
Experience: 1 years | English: fluent

Facial Emotion classification with limited data source.
Right now I work in other speciality, but I was always interested in ML.

My higher education profile was "Intelligent Data Processing Systems". For example, I wrote my thesis for a bachelor's degree on the topic of image classification of emotions, where the problem under study was a limited number of dates (> 5000 images in total).In addition, I have implemented other ML problems, dialog context classification, regression, Markov Hidden Model, etc.
The most important for me is to participate in interesting project.
Position: Machine Learning Engineer
Category: Data Science
Experience: 2 years | English: fluent

- KPI FICT Master's Degree (123 Computer Engineering)
- A lot of self-improvement activities in the field of work
Samsung R&D 2 years of experience
    > Text recognition. Model training and optimization for mobile.
    > Research of different neural network approaches for performance enhancement
    > Mobile demo development with Java + C++
    > Model optimization techniques (pruning, quantization, distillation)
Position: Machine Learning Engineer
Category: Data Science
Experience: 2 years | English: upper


Self-employed freelance with 2 year experience
- Computer vision (OpenCV, TensorFlow, PyTorch, Keras)
 1. Car brand recognition using numpy and opencv for work with images and augmenting them, also 
 using AWS S3 and SageMaker for cloud computing
 2. Price tag recognition using opencv for text on image
- Machine learning (Scikit-learn, NumPy, ARIMA)
 1. Writing diploma for time series analysis using ARIMA model
 2. Finding similar products for recommendations in the web store using KNN method from scikit- 
 learn
- Telegram bot development 
 1. Developing student council telegram bot for PMA KPI department
 2. Developing telegram bot for predicting heart attack using scikit-learn
 3. Developing telegram bot for online school using databases and payment system
- Programming, advanced mathematics, Physics tutor
- Backend development (Django, Tkinter)
 1. Developing backend using Django for rating drinks website
- Frontend development (Figma, HTML/CSS, JavaScript)
 1. Developing fronted for rating drinks website using Figma for scretching and HTML/CSS with 
 JavaScript for realization 
 2. Developing frontend for board game website using Figma for scretching and HTML/CSS for 
 realization
Position: Machine Learning Engineer
Category: Data Science
Experience: 6 years | English: upper


Senior Software Engineer
Jul 2021 – Current
Stack: PHP · OOD · OOP · Laravel Framework · Git · JavaScript · Docker · PostgreSQL · Microservices · DDD · Kubernetes · Redis · Continuous Integration and Continuous Delivery (CI/CD) · Symfony
 
Software Engineer
Apr 2019 – Jun 2021
Stack: PHP · Machine learning · Python · Deep Learning · Tensorflow · MySQL · OOD · TDD · OOP · Laravel Framework · Git · Docker · Amazon Web Services (AWS) · OpenID Connect · OAuth 2.0 · REST APIs · Microservices · Kubernetes · Redis · CI/CD
•	NLP text classifier (Python, Tensorflow) 
•	working with Google APIs 
•	sql queries optimization 
•	collecting statistical data from different application domains 
•	cloud call center using AWS (Amazon Connect, Kinesis Streams, Lambda functions, S3) 
•	implementing CI/CD pipelines (AWS ECR, Github Actions) 
•	migrating monolith applications to microservices architecture
•	containerization of existing applications (Docker) 
•	designing and implementation of ID provider 
•	containers orchestration (Amazon EKS, Kubernetes, Istio) 
 
PHP Developer    
July 2018 - April 2019
Stack: PHP · MySQL · OOD · OOP · Laravel · Git · JavaScript 

PHP Developer    
August 2017 - July 2018
Stack: PHP · MySQL · OOD · OOP · Laravel Framework · Git · JavaScript
I am looking for a role in a team where I can apply my skills and knowledge in Machine Learning to solve real-world business problems. I value teamwork and collaboration and want to work in a creative and innovative environment where we can develop and implement new Machine Learning methods.

Additionally, I am seeking a role that will allow me to continue growing and developing in the field of Machine Learning. I want to work with experienced colleagues who can teach me new methods and technologies. I am also interested in participating in projects that have a real impact on society and the industry.

Ready to take the test assignment.
Position: Machine Learning Engineer
Category: Data Science
Experience: 1.5 years | English: upper


Sep-2022 - Dec-2022 - EPAM Systems
EPAM Project Description: AIOps Initiative
Project Role: Developer
Responsibilities: 
•	Manually created dashboards for application monitoring
•	Worked on automation of dashboards creation
•	Enhanced and used Python scripts to create alerts via API
•	Collaborated on creating parsing scripts using Python and regular expressions
Tools and Technologies: 
-	NewRelic (UI & API) 
-	Python 
-	Jira

Jun-2022 - Aug-2022 - EPAM Systems
EPAM Project Description: Non-Prod project
The idea was to create ML model that would recognize Russian propaganda
Project Role: Developer
Responsibilities: 
•	Used Docker to deploy Splunk
•	Worked with a teammate on cleaning and preparing data from posts and replies which he scraped data from VK that related to war
•	Ingested data into Splunk
•	Created detailed data analysis reports
•	Explored Splunk ML Toolkit, Splunk NLP Text Analytics 
•	Explored various NLP and ML algorithms, test pre-processing techniques, best practices
Tools and Technologies: 
- Splunk and its apps
- Python and libraries for ML and NLP
- Docker

Feb-2022 - May-2022 - EPAM Systems
EPAM Project Description: Short-term MVP
Project Role: Developer
Team Size: 3 Software Engineers
Project Management: Scrum
Responsibilities: 
•	Developed dashboards and alerts for monitoring
•	Performed log onboarding and parsing, creating sourcetypes
•	Took part in troubleshooting and configuration changes
Tools and Technologies: Splunk DBConnect and other Splunk apps, Splunk

Oct-2021 - Jan-2022 - EPAM Systems
Project Description: Pre-Prod EPAM DE Lab Project - ETL 
The goal was to automate the ELT process from AWS RDS into Snowflake and build reports in Tableau
Project Role: Data Engineer
Team Size: 5 students
Project Management: Scrum
Responsibilities: 
•	Modelled star schema for DWH with SCD
•	Created automated data flow from AWS S3 (data was in CSV files) to DWH in Snowflake (star schema). Loaded data using Snowpipe and transformed it with the help of streams, tasks, stored procedures, and transactions
•	Collaborated with teammates on creating reports in Tableau and writing script Python
Tools and Technologies: 
- AWS (RDS, S3) 
- Snowflake  
- Tableau 
- Jira
- SQL 
- Python 
- DWH, star schema 
- Scrum
- Github

2019-2023 - student at the Ivan Franko National University of Lviv, Faculty of Applied Mathematics and Informatics, 
System Analysis
Position: Machine Learning Engineer
Category: Data Science
Experience: 1 years | English: intermediate

PhD (2022-current), master (2020-2022), bachelor (2016-2020) in applied mathematics:
Qualification work in the field of convolutional neural networks for generating the physical characteristics of materials; optimizing the structural parameters of structures.
Research articles on the visualization of multidimensional spaces, structural optimization, fault detection, mathematical methods of structural analysis.

Engineer researcher in the field of analytics of the dynamics of machine-building structures: Conducted research on the collection, processing and AI analysis of vibration data of dynamic systems for the fault detection.
Internship and subsequent full-time employment as a rotor dynamics engineer in engineering company SoftInWay (USA, Kharkiv department) - established and carried out a procedure for testing mathematical methods for analysis the dynamic characteristics of rotational systems.
Skills:
• Machine Learning: Scikit-learn, CatBoost, XGBoost, LightGBM, Optuna, FEDOT;
• Deep Learning: PyTorch, Keras, TensorFlow, OpenCV, huggingface, RASA;
• CV&Geo: OpenCV, rasterio, shapely, geopandas, Planet API;
• Development: Python, SQL, git, Docker, Flask.

Experience:
Data science engineer. 
Jan. 2022 - now (1+ year)
• Developed a service for sketching images from drones: R&D of a robust bundle adjustment algorithm, seamless merging of images, delivery using a GCP etc.;
• Participated in the development of auto-ml services for urban place analytics - developed and supported services for classification, forecasting, clustering tasks on tabular and telemetry data;
• Performed geospatial analysis - developed satellite images segmentation and classification models for AGB estimation and flood detection. Used rasterio, shapely, geopandas, worked with web services and API for satellite imagery retriving;
• Engaged in R&D projects in NLP field - developed context-free Q&A bot for web pages, researched IF&SF task for automated testing of web applications.