lang-uk/recruitment-dataset-candidate-profiles-english huggingface dataset:
Position: Machine Learning Engineer
Category: Data Science
Experience: 4 years | English: fluent

Successfully implemented an AI-based License Plate Recognition (ALPR) system in over 150 facilities across multiple countries, improving efficiency and accuracy in parking gate operations.
Developed a powerful algorithm with over 99.6% accuracy for recognizing vehicle plate numbers, colors, and make/models, enhancing the performance of the ALPR system.Spearheaded the development of a comprehensive churn prediction pipeline, retaining over 500 clients on a monthly basis and preserving valuable customer relationships. Developed an innovative algorithm integrating real estate prices with proximity to essential amenities, providing a comprehensive scoring system for informed buying decisions. Achieved unprecedented levels of accuracy in depth estimation for 3D reconstruction, reducing the margin of error between estimated depths and ground truth measurements.
At Parqour, as a CV Engineer, the focus was on implementing an AI-based License Plate Recognition (ALPR) system for parking gates across multiple countries. The accomplishments include improving the existing ALPR systems, developing a highly accurate algorithm for recognizing vehicle plate numbers, colors, and make/models, and optimizing the ALPR system's performance on various devices. Additionally, managing a team of computer vision engineers and collaborating with a team to optimize the ALPR system's functionality for different parking areas showcased strong leadership and teamwork skills. In the role of a Data Scientist at KCELL, the main responsibility was to develop a pipeline for churn prediction and forecasting. This involved designing a robust data warehouse, leveraging technologies like Hadoop, Hive, and PySpark to establish an efficient churn prediction pipeline, and successfully retaining clients through effective pipeline implementation. At BI Technologies, the focus was on real estate evaluation and liquidity assessment. The achievements included constructing an extensive database, establishing a robust data warehouse and machine learning pipeline, and achieving impressive accuracy in house price estimation.As an Assistant Researcher at Nazarbayev University, the work primarily involved investigating feature correspondences for 3D reconstruction and depth estimation. At Petrel AI, the main focus was on developing a robust model for vehicle plate number detection and recognition.The work at the University of Maryland involved developing a specialized model for detecting sun angles on crop and face images, aiming to improve the accuracy of image tamper detection. This required conducting in-depth research on CNN architectures and image recognition techniques to design a custom CNN model tailored to sun angle detection. The ability to apply research findings to practical applications demonstrated strong expertise in the field of computer vision.
Position: Machine Learning Engineer
Category: Data Science
Experience: 2 years | English: fluent


Automation of moderation(user info, photo, text).
Building and maintaining production-ready ML systems from scratch.
Computer Vision, NLP, Tabular data.
Antifraud/Antiscam systems. 

Wide experience in web development. Developed several web applications for data mining, data labeling.

Interested in Computer Vision, Generative AI
Position: Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: upper

- Won the Minsk-Riga Startup Train hackathon, and then founded a startup with the team
- With this startup we got into the largest Belarusian startup accelerator TechMinsk by Imaguru and successfully finished it.
- During the "School of Future CTO" was distributed to the team and we won the nomination "Best Business Negotiators"
BiQuants – Deep Learning Engineer
11.2020 - Present
	Development of an analytical system based on computer vision for one of the world's largest fast food companies. The system detected what was going on inside and outside the restaurants, counted the visitors, the time they spent in the restaurant, thereby allowing to optimize the restaurant workflow. Deep learning models developed and trained using PyTorch and DarkNet, inference pipeline build on TensorRT. In addition, I was engaged in data preparation process, and when the amount of data became too large, I began to manage a team of 4 people who are engaged in data preparation/data engineering. 

Phoenix Education & Yandex.Cloud – "School of Future CTO"
08.2020 – 09.2020
	Track – Data Science (Computer Vision). We developed a system of tracking, reidentification and classification of people in retail stores, as well as a system for determining the location of a person in space. Tracking and reidentification system works on Intel CPU in real time with the help of OpenVINO technology. Classification works with PyTorch.

BelSpaceAgency – Deep Learning Engineer
10.2018 – 10.2019
	Developing of on-device deep learning object detector and tracking on TensorFlow, which recognize objects in real-time and predicts distance to the object. Also, the optimization of the entire system for weak devices like Jetson TX1/2 or for devices with weak graphics cards that are not intended for deep learning. 

Artyline – Co-Founder & CTO
08.2018 – 11.2020
	Developing of on-device deep learning real-time object detector on TensorFlow, which recognize UX-sketches and convert them into a ready-to-use application layouts. This system works on Apple devices supporting CoreML and in real time can visualize your sketch. Besides the direct development, I am also a product manager in a team with iOS developers, Q&A Engineer and UX/UI Designer.
Position: Machine Learning Engineer
Category: Python
Experience: 7 years | English: upper

- Audio Chord Estimation model achieves SOTA result (but less number parameters)
- Reduced resource usage: trained models on TPUs and edge devices such Nvidia Jetson costed less than rent GPU
- Data pipelines on lambdas that reduced resource usage and cost.
- Top pandas contributor. My code is executed on ~15M devices monthly according to PyPI Stats.
- Core developer of system that performs > 20M requests per day.
- Developed reliable processing System that was handling more than $3M per month.
- Built the model for audio chord estimation using CNN and LSTM (pytorch, TPU, tensorboard).
- DSP, data collection and preprocessing (python, scipy, pandas, AWS)
- System Design and backend development: Python (async), Postgres, ELK, Redis, Kubernetes, AWS, Pub/Sub
- Core developer in British startup (over 1M active users): improved core system, developed a bunch of features, significantly reduced resource usage, improved stability of ecosystem (async python, ELK, Postgres
- Top US insurance company: set up ETL pipeline, built several services (python, flask, aws, pandas, redshift)
- FinTech startup: design and implementation the payment processing system, e-currency exchanger, BTC-currency exchanger
Position: Machine Learning Engineer
Category: Data Science
Experience: 4 years | English: upper

ML-geek:)
Business areas experience:
- raw text segmentation and analysis;
- keywords extraction and text generalization
- sales funnel optimization;
- advertising and dynamic pricing;
- speech recognition;
- statistics and predictive models;
- any machine learning problem

Algorithms:
- reinforcement learning (MAB, CB, QL, DTS, DQN)
- deep learning (DNN, CNN, RNN, GAN)
- statistical machine learning (Bayesian, Linear, Polynomial, Generative, Instance-based, Ensembles, etc)
- time series (ARIMA, LSTM, CNN, etc)
- natural language processing: 
text cleaning and wrangling, representation learning (BOW, TF-IDF, embedding), similarity measurement and clustering, relation extraction, semantic analysis, sentiment analysis, and keywords extraction (BERT, Rake, Yake, PageRank, etc).

Technical stack:
Programming Languages: Python 2/3, R
ML: Scikit-Learn, XGBoost, LightGBM, TensorFlow, Keras, PyTorch, Ignite, Fastai, Skorch, Xlearn, Gym
NLP: Spacy, NLTK, Gensim, TextBlob
Data analysis: Pandas, SciPy, NumPy, MS Excel
Data Visualisation: Matplotlib, Bokeh, Seaborn, Plotly, D3.js
AutoML: PyCaret, MLflow, Auto-Sklearn
DBMS: SQLite, PostgreSQL, BigQuery, MySQL, MongoDB
Web Scraping: BeautifulSoup4, Scrapy, Splash, Selenium
Big Data: Hadoop, Spark, MLlib
Web: Flask, Aiohttp, Django-rest
Cloud: AWS, GCP 
Other: Linux, Git, Docker, ElasticSearch, Cython
Professional interests: natural language processing, recommendation systems, bioinformatics, and medicine. But also interested in any area where ML could be implemented. 
Expected an exciting project and a friendly team:)
Position: Machine Learning Engineer
Category: Data Science
Experience: 4 years | English: upper

BSc _ Computational Math and Cybernetics LMSU
MSc _ Telematics Engineering University of Carlos III Madrid
Publication at IFIP 2018
Participation at several conferences such as IFIP 2018, SIGCOMM 2018.
C++, Git, Python, Machine Learning, Research, Linux, scikit-learn, Neural Networks, Computer Networks, Routing, Tensorflow, Keras, GAN, RNN, Mathematical Statistics, Bayesian Methods, Reinforcement Learning.

Previous experience as Software Engineer and Research Assistant in Europe.
Real world problems that a lot of mathematics embedded and career opportunities.
Position: Machine Learning Engineer
Category: Data Science
Experience: 2 years | English: upper

Created more than 6 pet projects in different fields of Machine Learning (NLP): Question Answering, Named Entity Recognition and Relation Extraction, Sentiment Analyses, Text Summarization, Topic Modelling, and Text Classification.

Hackathons Experience:

1) Hack4retail:1st place on the leaderboard
Task: Optimize dark store items placing
Solution: Optuna, Genetic Algorithms

2) PTI Hack 2022: 3rd place
Data Science Hackathon 
Task: Predict the probability of deal closing
Solution: building a Classifier, then using LightGBM and CatBoost separately, then stacking them to predict the probability
Commercaial Experience

Machine Learning (NLP) Engineer at Knowledgator Engineering (a Life Sciences data analytics software company).


Pet Projects

1) NLP Dialog System
One-Class Classification, Question Answering,  Natural Language Generation, Text Summarization, Sentence Similarity
Task: Build a bot to answer Data Science interview questions and perform NLG
Solution: OneClassSVM, AutoModelForSeq2SeqLM, Sentence Transformers, Cosine Similarity, BERT pre-trained on SQUAD Dataset, DialoGPT

2) Twitter Sentiment Classifier
Multiclass Classification
Task: Classify tweet sentiments
Solution: TF-IDF, LinearSVM, t-SNE

3) Chemical Named Entities Extractor
Named Entity Recognition
Task: Extract chemical-named entities in IOB annotations from the CHEMDNER corpus
Solution: CRF, BERT

4) CPU Recognition Pet Project
Data Engineering, Computer Vision (Oct 2021 - Feb 2022 )
Worked in a team of 4 people. Responsibilities: Parsing and labeling dataset for CPU recognition with YOLO
Seek for gaining new knowledge, extending experience, and implementing SoTA solutions to real-life tasks. Willing to join the professional team to work on ML Projects
Position: Machine Learning Engineer
Category: Data Science
Experience: 0 years | English: intermediate


Completed Course: Zero to Mastery Deep Learning with
TensorFlow (06/2022 - 08/2022)
Used skills: TensorFlow, Keras, NumPy, Pandas, Matplotlib, Sci-Kit Learn, Git.
- Studied Techniques of Machine Learning, Deep Learning and Computer Vision.
- Used Git to demonstrate my progress in this course.
Completed Project: Airbus Ship Detection Challenge 
(08/2022 - 09/2022)
Used skills: TensorFlow, Keras, OpenCV, NumPy, Pandas, Matplotlib, Git, Jupyter Notebook.
- Used Git to store this project.
- Used Jupyter Notebook to check the dataset from the Kaggle challenge.
- Used Techniques of Machine Learning, Deep Learning and Computer Vision.
Professional growth. Flexible working schedule.
Position: Machine Learning Engineer
Category: Data Science
Experience: 0 years | English: upper

Highly motivated team leader with experience in automotive industry. Seeking to transition into a data scientist/machine learning engineering role to utilize my strong technical background, database creation experience, and passion for data analysis. Fluent in English and German, with excellent communication skills. Ability to lead cross-functional teams, optimize processes, and drive project success. Skilled in collaborating with customers to understand their requirements and find best solutions for innovative products.   Eager to contribute my leadership experience, analytical mindset, and problem-solving abilities to drive successful outcomes in the field of machine learning engineering.
Created from 0 a database for collecting qualification assessments and organization of trainings. Used for this MS Access and VBA. Finished course Data Science fundamentals from DataRoot University. Finished internship for backend web development (have a pet project).
Position: Machine Learning Engineer
Category: Data Science
Experience: 2 years | English: intermediate

Fourth-year student of Institute for Applied System Analysis (IASA) of National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute".
• Credit Card Fraud Detection System using Logistic Regression, Support Vector Machine, Random Forest and Naive Bayes (scikit-learn)
• Deep Image Classifier using simple custom CNN (TensorFlow)
• Deep Image Classifier using MobileNetV2 with Fine Tuning (TensorFlow)
• Named Entity Recognition (Hugging Face)
• Document Text Recognition and Classification using LayoutMv3 (Hugging Face)
• DB "Hotel Complex" with web user interface (SQL + PHP) - Coursework
I want to apply what I already know to real-world problems and also improve myself by gaining new experience. I would like to work in a team for the long term, with further prospects for joint growth.
Position: Machine Learning Engineer
Category: Data Science
Experience: 0.5 years | English: intermediate


Currently I am learning in courses would like to improve my knowledge in Python to build application's, debug, develop and implement projects. My strength are communication skills, flexibility, teamwork. I easily work with a large amount of information, learn quickly.
Position: Machine Learning Engineer
Category: Data Science
Experience: 0.5 years | English: fluent


Currently I don't have commercial experience in this branch, but I have strong knowledges in ML/AI so I am pretty sure that I'll be familiar with the tasks I can face up with during the job. Durin g my learning projects I have worked with NLP, Tensorflow and Pandas tools, ChatGPT integrations. I prefer Python for work, but OK with learning R if needed. Also I have worked with SQL syntax, so I feel comfortable with big databases.
I expect interesting and complex tasks that would be challenging, since that how I can improve my skills, also it would be great to work with different integrations. Probably, I would not be interested in work connecteds with testing the AI.
Position: Machine Learning Engineer
Category: Data Science
Experience: 2 years | English: fluent

TensorFlow Developer Certificate - October, 2021, by Google

Google Cloud Skill Badge: Perform Foundational Data, ML, and AI Tasks in Google Cloud  - Google 

IELTS Academic - 8.5, British Council Azerbaijan
Currently working as Machine Learning Engineer at Azercosmos. I have a total of two years experience as Machine Learning Engineer. 

My previous job responsibilities include model development, deployment, creating data processing pipelines, writing backend and creating SQL databases.

Past projects:
- Medical computer vision via transfer learning
- Image segmentation task with UNet architecture
- Single-class image classification on Google Cloud (Vertex AI)
- A web-app based on Flask to scrape and reorganize contents of Azerbaijan National Library
-Deep learning based spelling correction software, detexter.com

Deep Learning frameworks: Tensorflow, Keras
Machine Learning framework: scikit-learn, fast.ai
Data processing: Pandas, NumPy
Understanding concepts of OOP and unit testing
Understanding of algorithms, data structures, algorithms, statistics, probability theory, linear algebra
NoSQL Databases: MongoDB
SQL Databases: SQLite, MariaDB, MySQL, PostgreSQL
Working with cloud providers: Google Cloud (DataFlow, DataProcç BigQuery, Vertex AI)
My main expectations are:

(1) Financial stability
(2) Contributing to an impactful project
(3) Improving my hard and soft skills
Position: Machine Learning Engineer
Category: Data Science
Experience: 11 years | English: fluent


Data Mesh / Fabric - design and implementation, provides flexible, resilient integration of data sources across platforms and business users, making data available everywhere it’s needed regardless of where the data lives (AWS, Azure)
	Cloud-native platforms – developing business-focused applications that leverage advanced  technologies. It allows businesses to build new application architectures that are resilient, elastic, and agile — enabling companies to respond to rapid digital change
	Composable applications - next step in microservices approach, applications are built from business-centric modular components (Python, Golang, Javascript, Typescript)
	Decision intelligence - providing companies with methodology and roadmaps to improve organizational decision making. It models each decision as a set of processes, using intelligence and analytics to inform, learn from and refine decisions
	AI Engineering – leading projects to automate updates to data, models, and applications to streamline AI delivery.
	Generative AI  - investigating a variety of opportunities (“known unknowns”) to learn about artifacts from data, and generates innovative new creations that are similar to the original but don’t repeat it. Generative AI has the great potential to create new forms of creative content, such as video, and accelerate R&D cycles in a variety of business fields.
	NFT - use cases (gaming, music industry), NFT Smart Contracts, NFT Minting, IPFS storage, and NFT Security (ERC-721 Smart Contracts)
Position: Machine Learning Engineer
Category: Data Science
Experience: 1 years | English: fluent


Data Migration Consultant
December 2019 - March 2020
Rapid deployment of data migration platform for global customer's Data Factory
• Analysis and collection of requirements to the data in accordance with system's landscape and business specifics.
• Creation of data mappings of source and target systems with conversion rules for data transformation set up.
• Collection and definition of data quality requirements and creation of documentation for data integrity checks and post-load verification. Testing of upload and extract functionality.


Data Engineering, Management and Analytics		                                                                             2014 – present time
• Dataset creation via data scraping, text parsing, data collection using APIs, logging. 
• Data migration from different sources and its consolidation.
• Data cleaning, enrichment and transformation. Data labeling. Set up of structured or unstructured data storage. 
• Scripts and bots for data workflow automation.
• Data visualization (dashboards, summary tables, plotting).
I have 10+ years of experience with data collection, pre-processing, migration and integration.
I actively expand my knowledge and toolkit in machine learning and data science fields and would do my best to add value to team which dynamically grows in this areas.
Position: Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: intermediate

+ Master degree in Applied Physics and Plasma Physics, strong mathematical background
+ Participated in Kaggle competitions
+ Developed Reinforcement Learning framework including q-learning, policy gradient, actor-critic algorithms applied to game playing. Used technologies are Python+Pytorch
Data Scientist
 + Developed models to forecast stock exchange prices (time series data) using Python + TensorFlow/Keras
 + Developed ML framework for training and validating time series models
 + Done educational project on Computer Vision on Kaggle - Bengali.AI Handwritten Grapheme Classification
 + Gathered and marked economic news data
 + Integrated news data into time series prediction model with recurrent neural networks
 + Analyzed data of S&P500 stock prices, news articles, fundamental economic data, created reports for stakeholders

Machine Learning Engineer
 + Developed MVP of Neuroseed machine learning service for uploading and storing datasets, training, validating, making ML models inference on Python+TensorFlow/Keras
 + Designed and implemented REST API for MVP using Python+Falcon
 + Created and ran services in Docker containers
 + Worked on Proof Of Concept of Transfer Learning for various ML models
 + Administered MongoDB database
 + Made automatic tests with PyTest

Personal Interests
 + Deep Reinforcement Learning
 + Have used Ubuntu Linux for work for 4+ years
 + Developed GUI programs in QT C++
 + Have experience with Arduino microcontrollers and Raspberry Pi microcomputers programming
 + Have game development and modification experience Unity/C#
I'm looking for work on machine learning, neural networks, NLP, Reinforcement Learning.
Position: Machine Learning Engineer
Category: Data Science
Experience: 1 years | English: upper


- Development of AI models for Noise cancellation and Voice cancellation 
- Work with audio data using Digital Signal Processing
- Research on modern audio processing techniques
- Development of evaluation metrics
- Data cleaning and preprocessing
Position: Machine Learning Engineer
Category: Python
Experience: 2.5 years | English: upper


Development of end-to-end solutions for many NLP tasks such as named entities extraction, key phrases extraction, topic modeling:

1) Understanding needs of product owners, participation in custdev sessions and transferring business problems to technical tasks
2) Building data annotation tools with Django and Angular
3) Development of pipelines for data collection and preprocessing
4) Running and managing experiments with machine learning models such as BERT, LSTM, Gradient Boosting, and so on.
5) Model inference optimization with ONNX and product quantization
6) Deploying models in production with FastAPI and Docker
Position: Machine Learning Engineer
Category: Python
Experience: 5 years | English: intermediate


- development of various neural networks for processing Ukrainian languages texts ( coherence evaluation, coreference resolution, noun phrase detection )
- development of web-interfaces for embedded devices 
- development of custom Linux-based firmwares for network devices
-integration of crypto modules into embedded devices 
-development of e-commerce sites from scratch 
-development of a complex system for OCR processing of custom images