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

I won an Erasmus grant for the 6-month master's exchange program at the University of Groningen and KPI, where I studied Artificial Intelligence.

 Additional Projects
Deep Learning Creator School Autumn 2021 (in progress) - built fake video classification DL model, mentored by Reface head of ML
Tweet sentiment analysis Spring 2019 - the final project of the course mentored by Grammarly NLP Team Lead, presentation in front of 100 people 
Recommenders System based on Sentiment Analysis 2018 Feb - May- production like architecture to compute recommendations based on reviews 
Handwriting Recognition for Degraded Historical Manuscripts 2018 Apr - July - research in computer vision techniques, wrote the scientific paper 


Certification
Natural Language Processing with Attention Models Coursera 2021 Feb
Fundamentals of Reinforcement Learning Coursera Jul 2021
Natural Language Processing by Projector 2019 Mar - May
Kyivstar Big Data School Course 2017 Nov - Dec
Oracle Database SQL Expert Certificate 2016 Aug
I am a motivated Machine Learning Engineer with mathematical background having 3+ years of experience in tabular data, big data, NLP and CV.

Reface, Data Scientist (Sep 2021 - present) 
dynamic pricing model, recommendation research 

MGID, Machine Learning Engineer in Big Data Team (Nov 2018 - Aug 2021)
Summary 
built ML models for the large volume of data (2T+ daily)
delivered business value and influenced product decisions
conducted researches in the field of NLP and DL
Projects 
Gender classification model - 60% traffic coverage. Wrangled terabytes of data, feature selection on sparse categorical data, implemented neural net 
Identical and similar image finder - 1% growth in revenue estimated with A/B testing. Developed the image similarity system to cluster duplicates or similar pairs with 2000 of new images coming daily 
Classification of video demand availability - got the ensured recall for the positive class of 95%, inference time < 20 ms, ensured that the model works with no degradation in real time 
Image tag multi-label classification - decreased manual tagging by 20 percent. Transfer learning, iterative process with another department.
URL Categoriser research - dataset gathering ideation, hierarchical text  classification models overview, analyzed papers with state-of-the-art methods
User look-a-like clustering - found users who behave like converting customers, approximate nearest neighbor search in the dataset of million samples
I am passionate about Deep Learning and want to work in this domain. I value a challenging enthusiastic and active environment. Previously worked in product companies, I don't want to work at outsourcing.
Position: Machine Learning Engineer
Category: Data Science
Experience: 11 years | English: fluent


I am a senior machine learning engineer, mathematician and data analyst. I majored in computer science with applied mathematics. In other words, I have good knowledge of calculus, algebra, neural network, optimization, probability, statistics and time series analysis. I worked on several positions such as machine learning engineer, computer vision engineer, natural language processing engineer, data scientist and software engineer in the past. So, I have totally 11 years of experience and deep understanding in AI and related domains. 
And currently I am researching about the NLP, specifically LLM including ChatGPT, GPT-3, 4, BLOOM models and transformer, BERT models. Really, I have experienced these things in the developments of chatbot, sentiment analysis, text generation models.
Also, to increase my ability I am reading the state-of-the-art papers and understanding in real time. Since I majored in applied mathematics, I am not afraid of new challenges.
Learning new things is a part of my life.
Lastly, I am very familiar with python, R, matlab, C, C++ programming languages
Please let me know if you are interested in working with me. Thank you.
Position: Machine Learning Engineer
Category: Data Science
Experience: 5 years | English: fluent


I am currently working as a Software Engineer/Product Engineer in a company called Technomind Digital Systems in Turkey. Since the company is an entrepreneur company, I also fulfill the task of creating projects, distributing and following up the tasks, creating the project budget, meeting with the investor and the relevant companies in cooperation with the company, apart from my own duties.
I was working on product safety and regulations at the EKSON Otomasyon Teknolojileri company I worked for before, I was following the project I wrote to get support from the government and I was providing product supplies.
During and after my university period, I worked on the writing of 3 different government-supported initiative projects, the creation of the budget and the team, the follow-up and reporting of the project.
In addition, I am in the last semester of my master's degree at Yildiz Technical University, Department of Mechatronics Engineering. The subject of my master's thesis is about A Transfer Learning Based Hybrid CNN Model for Diagnosis of Breast Cancer with CAMELYON dataset. During my graduate education, I have published an academic paper on the comparison of machine learning methods for the classification of EMG signals. With my experience, I am confident I would be a valuable asset to your team. 
Due to my mechatronics engineering background, I also contribute to electronic projects in the company from time to time. In the pathology scanning device project, we have applied for a patent, among which I am the co-founder, due to my contributions in the field of software.
Position: Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: fluent


I am experienced Research Engineer with experience in the fields of Computer Vision, Signal Processing and NLP. Skilled in Python, C++, Machine Learning, and Deep Learning. Worked with Tensorflow, PyTorch libraries to solve problems such as machine translation, image recognition, object detection, motion recognition and time series. 
Worked with Rassberry Pi board to collect and process Wi-Fi radio signals for human action classification. Developed DL-based solution for the camera autofocus system via UWB Radar. Trained and deployed object detection models on devices. Developed Q&A NLP ChatBot platform for OpenSource domain. Investigated how commercial VoIPs influence the Detection model (prepared environment for automatic data set collection with different commercial VoIPs, developed model verification, took part in patent creation).
Position: Machine Learning Engineer
Category: Data Science
Experience: 2 years | English: upper

Subject Azerbaijan Mathematics Olympiad – 4th place in Republic
3 times ADA Academic Excellence Certificate
ACM ICPC participant
Google Hash Code participant
2 times CanSat National Competition Finalist Winner of education hackathon in Vilnius, Lithuania Winner of global hackathon in Tallinn, Estonia Finalist of IBA Hackathon in Baku
Master Degree Computational Linguistics Scholarship by Charles University
I am mostly specialized in NLP and interested in Computer Vision based on Prague. I am previously Erasmus+ Computer Science student of Vytautas Magnus Univeristy in Lithuania. I have a few years working experience in various companies along with my studies. My latest experience is Machine Learning Engineer in Prague. Before that I was working as Machine Learning Engineer specialised in Natural Language Processing. I did several internships in my bachelor about Data Science, Software Engineer. Development Experience in: Python, Machine Learning, NLP/Computer Vision technologies Tableau, Docker, Flask/Django, Java, Oracle Database, MySQL.
I would like to get freelance (one-time) or remote jobs. I would like to use my skills other in the area of Machine Learning or Data Science.
Position: Machine Learning Engineer
Category: Data Science
Experience: 0.5 years | English: upper


I created several pet projects with python, SQL and machine learning:
Laptop price prediction 
• Pre-processed and performed exploratory data analysis to prepare data for model training.
• Created visual diagrams (histogram, scatterplot, box plot) and demonstration of correlation between
variables using matplotlib, seaborn.
• OneHotEncoded categorical data columns and trained regression and tree models from the sklearn
library to predict laptop prices.
Diseases database web application 
• Constructed database with PostgreSQL, resulting in systematically streamlined data tables with diseases
data.
• Developed an intuitive web application with CRUD (create, read, update, delete) functionalities by
Flask framework and SQLalchemy library.
OCT image segmentation 
• Analyzed over 2000 image data of the eye retina with python libraries pandas, numpy, matplotlib.
• Interpreted compared 2D 3D U-net’s performance for OCT image segmentation, leading to the successful
identification of suitable parameters for improved accuracy.
• Analyzed the performance of a 2D U-net model informed by 6 different loss functions using accuracy and recall metrics.
Position: Machine Learning Engineer
Category: Data Science
Experience: 2.5 years | English: upper

Studying MSc in Computer Science (Artificial Intelligence) at the University of Tartu (Estonia).
I have 2+ experience as an ML/Computer Vision Engineer. I have worked with Satellite Imagery processing, OCR, and Object Detection/Segmentation.

Stack: Python, PyTorch, Docker, OpenCV, Geopandas, Rasterio, QGIS, Sk-Learn, Sk-Image, TesseractOCR, PySpark.

Now I'm looking for various DS/ML jobs. My main interests lay in Computer Vision, urban planning/mobility (geospatial), and big data fields.
Want to get more experience in cloud and big data processing. Also, open to more Data Science positions with decision making and hypothesis testing. No NLP.
Position: Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: upper


I have 3 years of commercial experience.  
I have worked with various machine/deep learning tasks.  For example:
 - Fine-tuning LLM. Promt engineering.
 - Creation of a classification algorithm for audio classification using Transfer Learning base on the MobileNet3 neural network architecture.
 - Data collection, feature development, and dataset creation for the clustering algorithm.
 - Creating a classification algorithm to solve the churn problem.  
- etc.
My English level is upper-intermediate.
Position: Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: upper


I have 3 years of industry experience in data analysis, designing and building Machine Learning solutions.

Worked with time series forecasting and recommendation systems.
- Time series: electricity price prediction, airplanes price prediction;
- Recommendation systems: personalized predictions for cinema corporation.
Would like to join a company with a passionate team of Data Scientists.
Position: Machine Learning Engineer
Category: Data Science
Experience: 5 years | English: upper


I have a diverse experience: a mathematician researcher in a scientific laboratory, an analyst and later director of the analytics department in a quasi-state company where BI, BigData and classic Data Science and Time Series tools was implemented, led a large machine learning team where we built various models in CV, Audio and NLP and  I even have experience in building a profitable small business from scratch. 

For now I am looking for a job as a Machine Learning Engineer.  I believe that my varied experience did not serve to weaken my knowledge of Machine Learning skills, but rather supplemented them with some sense of reality.
Position: Machine Learning Engineer
Category: Data Science
Experience: 2.5 years | English: upper

- Master's deegre in System Analysis
- Writing personal tech blog in the medium platform
- The finalist of a hackathon FTI_HACK in 2022
- The 3 place in a hackathon INT20H in 2021
I have a solid background in the development of ML/AI solutions.  
I have participated in the whole CI/CD in the ML solution. I have creted definintion for labeling team, extracted data from DB, scrapped web pages, aggregated data after the labeling process, trained language models, set up MLOps practices into the project, built and implemented knowledge graph structure, researched, etc.

# In the last Project my responsibilities and achievements were
- During project we as ML team developed security product from scratch.
- Data Cleaning/Aggregation/Analysis, as result I have developed data aggregation and preprocessing pipeline for NER task that improve model accuracy on 15% 
- Train transformer base model for NER task with Spacy 
- Research Slack and M365 APIs, after it prepared data scheme and initial fraud detection implementation POC using knowledge graph which were moved into production
- Proposed and implemented MLOps architecture for model training process which reduce team's time consumption (from Data Labeling to  Model versioning)
- Prepared model fine-tuning for fraud detection with 0.98 f1 score
- found and fixed bug which lost important information and reduce prediction score, so improved overall accuracy on 5%
- Research science papers and implement ideas into real-life
- Created architecture and developed a data anonymization pipeline on medical conversational data
- Web Scraping for the creation a dataset
- Extracting text, and images from PDF, Scans, docx
- Generation dataset using LLM: GPT-3, GPT-3.5, Falcon, AI21Labs, etc
- Preparation of demos version for the customer about POCs and  implemented improvements
- Used asynchronous packages for working with huge amounts of data such as Dask and Ray
- Support the DevOps team while setting ML related tools on Kubernetes

=========================================
* ML Tools
Spacy, Neo4j, PyTorch, TensorFlow, HugginFace, MLFlow, Pachyderm, DVC, Weight&Biases, LabelStudio, Gradio, StreamLeat, Scikit-Learn, Padas, Numpy, Dask, Ray

* Cloud
AWS, Kubernetes, Kubeflow, Docker

* Non-ML Frameworks
Oracle, ElascticSearch, Cassandra
Consider ML-based solution
Position: Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: upper

RobotDreams - Computer Vision
Coursera - IBM AI Engineering
Coursera - SQL for Data Science
Coursera - Introduction to Machine Learning
Coursera - Deep Neural Networks with PyTorch
Data Root Labs - Data Science Fundamentals
I have experience in solving various ML problems. Worked with tabular data, computer vision, and NLP.
- Tabular data: customer churn forecast, LTV forecast, customer classification and clustering, scoring.
- CV: face recognition, similarity assessment, image classification, object detection, segmentation, pose estimation, avatar generation.
- NLP: text review score prediction, fake detection, text classification.
It is important for me to work with Data Science / Machine Learning professionals to learn from them.
Position: Machine Learning Engineer
Category: Data Science
Experience: 2.5 years | English: intermediate


I have four months of experience as Machine Learning Engineer. I have been involved in the development of these projects:
---- Recruitment analytics platform
An analytical platform that includes a comprehensive understanding of candidate profiles, assessing recruitment trends in terms of technology and skills, and assessing the candidate's compliance with specific job requirements, drawing on information from a variety of sources
---- Object detection
Developed object detector app classifies and localizes 90 different classes. Two different models were trained for an Android app and a web app prototypes, respectively. A web app was built with Flask, Android app was implemented in Java, as a base for this app was took TF mobile demo. Models were trained using TensorFlow Object Detection API.

Pet projects:
- A recommendation system that recommends open vacancies based on job seeker’s skills was developed during 24-hour AI Hackaton. Firstly, word embedding was used for analyzing job seekers’ CV and vacancies. Then, the system uses Word Mover's Distance to find suitable vacancies for a specific job seeker.
- A website built with Django, which recommends an anime based on user preferences using a SVD-based machine learning method of Scikit-learn library.

Skills:
– Python & Python libraries (NumPy, Pandas, SciPy, Matplotlib, Seaborn, TensorFlow, Keras, Scikit-learn)
– Machine Learning Concepts: learning theory (fitting models, validating and testing), EDA, pipelines for gathering and cleaning data
– Git, Linux, Docker, HTML, CSS
Challenging tasks and opportunities for professional growth, a friendly atmosphere, a flexible schedule.
I don't want to work on credit/insurance scoring tasks.
Position: Machine Learning Engineer
Category: Data Science
Experience: 0 years | English: upper

I have participated in CSC Hackathon 2021 and FTI Hackathon 2022. Placed 6th and 5th places respectively.
I have implemented these Machine Learning architectures(using TensorFlow): basic CNN(with padding, etc.), ResNet, YOLO, U-Net, Neural Style Transfer, Face Recognition Network(using Triplet Loss function), basic RNN, LSTM model for music synthesis, Character level language model(synthesis of dinosaurs names), Debiasing of Word vectors, Text Emojifying(using Embeddings), Neural Machine Translation(using Attention mechanism), Trigger Word Detection, Transformer Network(using Positional Encoding), NER, QA.
Also, I applied transfer learning to the MobileNet architecture. In DRU online school I gained experience in implementing ML algorithms from scratch and establishing a working ML system using Flask and Docker.
Position: Machine Learning Engineer
Category: Python
Experience: 5 years | English: upper


I have more than 5 years of experience in Software development and Machine Learning. I am looking for doing things that are valuable for people and are interesting in implementation overall ML/AI-area.

- 3 years as Machine Learning Engineer
- 5 years as Software Engineering (Python)

Stack:
- NumPy, Pandas, Scikit Learn (sklearn), matplotlib
- Tensorflow, Keras
- Python, Django, DRF, asyncio
- Docker, Docker Compose
- PostgreSQL
- AWS
- Git
- Jira, Asana
- Solidity, Blockchain
- Team and project management. Agile, Scrum, Kanban
Position: Machine Learning Engineer
Category: Data Science
Experience: 6 years | English: upper

In the current company, I quickly implemented the MVP version from scratch which was run on the client-side successfully and allow the company to deal with the client for developing a more extended version of the product.
I have solid experience in Software Development using C++ and Python programming languages. Last year I worked with Machine Learning projects, where my responsibility was integrating ML into production code and improve existing models. Furthermore, I have a related Data Science master's degree and during the last 2 years studying, I was doing small ml projects, especially related to NLP.  
So, I am interested more in Machine Learning project, NLP is preferable and want to develop myself in this area.
Position: Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: fluent

- Developed effective deep learning recommendation service based on Kubernetes.
- Implemented complex papers from scratch while improving on the results with my own ideas. 
- Developed architecture for machine learning based on Kubernetes cluster that is now used in the company I currently work in.
I have solid skills in C++ embedded development with 3 years experience working on RBS software in Ericsson. My machine learning experience is 1 year of design and deployment of algorithms for click and subscription predictions on sparse online data. In my free time I dabble with Unity (VR and ML agents) and implement reinforcement learning algorithms.
I hope for job to be meaningful and challenging while giving freedom in choosing solutions for the problems.
Position: Machine Learning Engineer
Category: Data Science
Experience: 4 years | English: fluent


I have studied Computer Engineering at Taras Shevchenko National University of Kyiv and have graduated with a bachelor's degree with honours. While in the university, I started working as an intern at the university's data computing center, where I worked on developing several websites using C# and JS. I had also started studying machine learning through online courses on coursera, where I mastered many different algorithms in all three branches of machine learning, and had completed many projects using them, such as neural style transfer, facial recognition, neural machine translation, galaxy type classification. At some point I got invited into an R&D team at Alcoras, where I worked on several projects using different technologies ranging from blockchain to machine learning, specifically I worked on developing a model for recognizing the value of "total" on pictures of receipts. There I discovered that I really enjoyed working as a ML engineer, and would like to continue working in that field.
Position: Machine Learning Engineer
Category: Data Science
Experience: 4 years | English: upper

- Deep Learning and Machine Learning: Experienced in various data science methodologies, showcasing a understanding of deep learning and classical machine learning algorithms.

 - Statistical Analysis and Model Interpretation: Well-versed in advanced exploratory data analysis techniques and model explanation tools.

 - Client Collaboration: Effective in fostering strong relationships with clients through daily communication, focusing on establishing goals, monitoring progress, and ensuring successful project outcomes.

 - ML Guidance, Idea Generation and Integration: Proficient in guiding team to develop and implement machine learning solutions, while generating innovative ideas and effectively incorporating them into projects, contributing to continuous improvement and delivering value-added solutions for clients.
I have worked with projects in various fields:

 - Computer Vision: 3D point cloud scene segmentation/part segmentation/object detection, 2D segmentation/object detection/classification, diagrams parsing (created big pipeline for this project: 4 different datasets with 4 different ML models);

 - Classical Data Science: predicting prices for car insurance; predicting whether delivery will be completed; multioutput regression (time estimation for roles in project);

 - Private ML: usage of differential privacy mechanisms;

 - NLP: text summarization, generation, classification (also using transformers), parsing pptx/pdf file formats with division into blocks/chapters;

 - Survival analysis
I want to work in the field of Data Science/Machine Learning/Computer vision and further improve myself in this; find good team to work with; gain interesting experience on hard projects.