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

Strong professional experience with tabular data, time series, 3d.
Deep knowledge in CV and NLP task proven with courses and competitive ML, pet projects.
Interested in lots of fields of AI: generative models, RL, etc.

Took part in lot's of hackathons and finished most of them in top-3
Instant education: lots of MOOCs passed
MSc in Applied Mathematics

A lot more info in my CV
3D Body Reconstruction project
preparation of a pipeline and testing hypothesis related to different cases of camera and body params to get useful insights about issues of the core 3d technology
developing fitting and analytical features for parametric body reconstruction pipeline
presenting insights to business-side colleagues (including C-level managers)
tech team lead
ML (linear and tree-based models, clustering models), DL (MLPs, CNNs), Python (scientific toolkit, libraries to work with 3d, basics of Django), PyTorch, SQL (PostgreSQL)


Predictive maintenance project
machines’ sensors time series data analysis, detection and forecasting possible machine failures
development of different improvements in existing ML and automatic reporting system
ML (tree-based models), DL (MLPs, CNNs, RNNs), Python (scientific toolkit), PyTorch, Matlab, FastAPI


FinTech project - HFT
research in the field of time series forecasting to enhance technology with latest advances in machine learning and deep learning, 
models validation and selection; productionizing of models, development of application for real time high velocity data stream processing
ML (linear and tree-based models), DL (MLPs, CNNs, RNNs), Python (scientific toolkit), R (dplyr, caret, shiny), TF, Keras, SQL (PostgreSQL)
Challenging tasks in fields of machine learning / deep learning
Knowledge sharing environment
Preferably R&D team

Minimum legacy
Minimum bureaucracy

Ready to consider non-profit initiatives as spare time volunteer (especially connected to Ukraine)
Ready to consider early-stage startups to help them grow
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 4 years | English: fluent


•• Application for improving sleep quality: participation in the development of sound classification pipeline (experiment design, data collection/labeling planning, and supervision, data preprocessing, modeling, running experiments, etc.), participation in the development and testing of voice activity detection algorithm, silence suppression algorithms, etc.
•• Application for learning foreign languages: mispronunciation detection. 
•• Malware detection & Data labeling Project: with the first aim of developing ML-based algorithms to detect malicious files for various file-types (e.g. Microsoft files, pdf, zip, etc.). And later to create ML data classification tool for an unsupervised data tagging process. I was responsible for dataset research and collection, creation of feature extraction module, performance optimization, file-type verification and extension matching, participated in modeling, evaluation, deployment, etc. (subject to NDA, no further details available). 
•• Research and development (under NDA): Retail Consulting discovery project; Customer Lifetime Value prediction Case Study; prediction of a Customer Response to renewal offers; Tags generation from the images of houses (object detection) PoC, etc. 
•• Medical data analysis accelerator development: EEG and HRV time series modeling and analysis for epileptiform seizure activity characterization in epileptic children, in perspective for further seizure detection/prediction. Writing papers, attending conferences, presenting research. 
•• Chatbot development (custom web, Telegram, FB Messenger, Google Assistant, Amazon): UX design, chatbot backend development, NLP Agent deployment and training, performance testing, chatbot analytics collection and processing, support.
•• Research and development of ML solutions for Kaggle competitions.
•• Participation in pre-sales research and project estimations.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 2.5 years | English: intermediate

Winning ML competitions connected with text data, image data, recommendation systems and structured data.
As a Data Scientist, I was working with:
 - numpy, pandas, matplotlib, scikitlearn, scipy, tqdm

 - Classic ML algorithms (LogisticRegression, SVM, RandomForest, Boosting, clusterization algorithms,  dimension reduction algorithms)

 - Text classification, text summarization, text parsing, named entity recognition (sklearn, nltk, gensim, TFIDF, Word2Wec, FastText, LSTM, BERT)

 - Image classification, image segmentation, landmark detection (keras, pytorch, torchvision, dlib, CNN, ResNet, DenseNet, MobileNet, U-Net)

- Git / Linux / Azure

Bachelor degree in system analysis
Intresting tasks, new experience
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: upper

As a Machine Learning Engineer, I worked on the development of a DeFi protocol that serves as a safety net for cryptocurrency investors. Our protocol was designed to protect investors from significant losses during market crashes while also providing an opportunity to profit from market pumps. One of my primary responsibilities was building a NLP model to analyze the overall sentiment of the crypto market. The NLP model was a crucial component of our protocol, as it allowed us to predict market trends accurately and adjust our protocol engine. I also collaborated with a team of developers and data scientists to ensure the smooth integration of the NLP model into the larger DeFi protocol.

I leveraged my expertise in machine learning, deep learning, and NLP, including building NLP transformer models like BERT and RoBERTa using TensorFlow, PyTorch, and Hugging Face, and working with NLP tools such as spaCy, NLTK, Scikit-learn, CuML, Hunspell, Pandas, and NumPy. My problem-solving skills, attention to detail, and passion for a fast-paced, collaborative environment were essential to my success as a Machine Learning Engineer. I also had excellent communication skills, enabling me to communicate complex technical concepts to non-technical stakeholders and work with cross-functional teams.

Besides my technical skills, I also had strong data analysis skills, including data preprocessing, cleaning, and visualization, and was comfortable working with large datasets, such as financial and social media data, using tools like Pandas and SQL. I was a quick learner, adaptable, and able to work both independently and as part of a team. My NLP expertise and these skills make me a desirable candidate for any company looking for a skilled Machine Learning Engineer focused on NLP.
As an experienced Software Engineer with a Master's degree in Computer Science, I possess a strong technical background and expertise in NLP and Transformers. With a results-driven mindset and a commitment to meeting deadlines, I excel at solving challenging problems and continuously improving my skills.

My expertise in NLP and experience with Transformers allows me to derive valuable insights from unstructured data and create effective data-driven solutions to complex business problems. I am proficient in NLP techniques such as sentiment analysis, named entity recognition, and topic modeling, as well as machine learning algorithms including regression, classification, and clustering.

Additionally, I am highly skilled in Python programming and machine learning libraries such as TensorFlow and PyTorch. With my technical skills and passion for Machine Learning, I am confident in my ability to contribute to any project that requires NLP expertise.
I am looking for an interesting project that will fascinate me, and in which I can prove myself.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 1 years | English: intermediate


Currently, I am one of the two people who ensure the functioning of the data science part in the company.
Price prediction for used heavy machinery
The entire project from the data cleaning to obtaining results for further deployment.
 
Previous projects:

Recognition of the authorship of a work of art;
Determination of the intersection area of two geometric figures in the image by the Monte Carlo method;
Abnormalities detection in the image presented in the form of an X-ray.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 4 years | English: fluent

Developed content-based posts recommendation system
Developed named entity recognition system for product names extraction based on neural network
Improved multilabel text classification model by 20%

Created models for batteries and generators runtime prediction
Created model and pipeline for extracting necessary information and tables from documents of pdf/image file formats
Created a framework for a faster development and deployment
Developed and deployed a face verification system for SaaS platform
Currently I work in telecom field and have fun with structured and unstructured data, Time Series, graphs, text and many others. I'm involved into all steps of projects from collecting requirements to deployment and support. At the current position I have developed and deployed 5 projects, created an unified framework for easier pacing into production

Have an experience in NLP. My responsibilities were:
- to get, analyse and prepare data
- to build NLP models using sklearn and TensorFlow
- to continue improving previously developed models 
- to create pipelines

I'm a graduate of IPT, Kyiv Polytechnic Institute in Applied Mathematics, Cryptology (June 2019), where I've got a great technical and mathematical background (statistics, linear algebra, mathematical analysis, abstract algebra, data analysis, Python, SQL)
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 2 years | English: fluent

- Company I worked for received a nomination at Ukraine Innovation Awards 2020.
- Master’s diploma with honours for graduating at the top 5% of the course.
- Obtaining Academic Scholarship throughout the entire span of studies at the university.
Experienced software engineer with several projects in Machine Learning. Have strong mathematical background with excellent programming skills. Have experience of solving real world problems using ML and DS. Design and development of  production-grade code and models.

Commercial experience:

- Designing and implementation of the SQL-database application for maintaining insurance broker’s business. 
- 3D reconstruction of a person's gaze in Python.
- Evaluating engagement by mapping gaze on real world objects in Python.
- Intelligent processing of large amounts of video using concurrent computations in C++.
- Analytical tool for researching and tracking biometrics in Python.
- Presentation of developed products at KRUPA 2019 conference.

Academical experience:

- Reinforcement learning based trading agent.
- Implementation of a novel artificial neural network to forecast financial data.
- Implementation of an artificial neural network for biometric processing.
- Lecturer in Data Science at DataRoot University courses.
- I am looking for a team of professionals who add value to business with data science. 
- Great team, professional and career growth are key things I expect from my future job. 
- Also I am interested in collaboration with international partners.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 2 years | English: fluent


Finished over 10 successful projects on data manipulation and visualization, model development in areas of finance, health care, and entertaining (predicting card approvals, investigating the influence of Dr.Semmelweis discovery of handwashing, investigating Netflix movies and guest stars in The Office). 
Used Multi-layer Perceptron classifier to predict what sort of people are more likely to survive based on the Titanic data.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: fluent


I am a machine learning and software engineer with 3 years of experience. I do research and development of custom AI and ML software, develop AI and ML models and help deploying them into production. I have considerable experience with python3 AI stack, as well as some experience in developing custom C++ solutions.

I am easy with new people and new environment. And I think I can make the chats at the cooler fun again :)

MediaPipe pose estimation for a sports app
Stack: mediapipe/C++
- Developed complex custom graph in protobuf and mediapipe Calculators with business ligic in C++

AI in healthcare: Multiple instance learning in computational pathology
Stack: pytorch, multiple instance learning, whole-slide imagery, python-openslide
- Developed a system for detecting certain genetic mutations in blood cells on whole-slide images.

LIDAR Vehicle detection and tracking system
Stack: C++, PCL, OpenCV, proprietary LIDAR sw/hw
- AI: Object tracking model training and integration.
- Software engineering: development of an algorithm for filling the points lost by the LIDAR due to weak reflectivity of the surface, segmentation and tracking performance improvements.

Diagram digitization
Stack: pytorch, OpenCV, scipy
- Solution architecture: bespoke graph-based algorithm development; graph compression by topological generalization; fully decomposed tech vision.
- AI: segmentation model training; synthetic data genereation + EDA + cleaning; review and testing of segmentation losses; training;
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 4 years | English: upper


I have 4 years of experience as a Data Scientist / Machine Learning Engineer.

I have commercial experience working on projects that come from the following areas of AI&Machine Learning:
 - sentimental analysis
 - object detection
 - general machine learning 
 - image outpainting
 - text-to-speech
 - AWS cost optimization
 - user web tracking
 - recommendation systems
 - research new state-of-the-art machine learning projects, methods and concepts
- data analysis, statistics
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 5 years | English: fluent

- Lead a team of 3 Data Scientists for 4 months, overseeing end-to-end ML product development.
- Together with 2 other developers took over and maintained in production a Machine Learning Framework developed and supported by a team of 20 in San Francisco.
- Got accepted to Cambridge for PhD in Civil Engineering with an industrial researched project for my company (didn't start the program bc of the changes at the company).
- TOEFL 30/30 for reading and listening with only 1 hour preparation for the test.
I have been working as a Data Scientist specializing in ML/DL for the past 4 years. In the past two years I focused on Computer Vision applications to ultrasonic sensor images from Oil&Gas pipeline inspections, which involved both designing and implementing the models, as well as deployment of the dockerised applications. My models have delivered 17% automation of manual pipeline analysis. 

Before that I have been responsible for end-to-end development of ML applications for supply chain, logistics and mechanical engineering use cases covering clustering, prediction, forecasting and linear optimization. 

During this time I have developed a solid expertise on classical ML as well as Deep Learning and Computer Vision methodology, productionizing models under rigorous review process, while upholding to high coding standards with unit test coverage, using Git and Jenkins for CICD and docker for containerizing ML applications. 

I have also worked on side projects in medical imaging, like instance segmentation of chest X-rays using U-net network architectures, and image super resolution of PET CT Scans using Variational Autoencoders. 

As a Data Scientist at GE, much of my work has involves investigating existing academic literature on ML and NN architectures in order to adapt the results and fine-tune the implementation to the use-case at hand. A good practice of mine for staying up to date with recent advancements

Tech Stack: 
- Python, Tensorflow/Keras, Scikit-learn, numpy, pandas, H2O, R;
- Linux, AWS (EC2, S3), Docker, Jenkins;
- Tableau, R Shiny, ggplot, seaborn, matplotlib.
I am interested in working on Machine Learning / Deep Learning applications, preferably in Computer Vision, but NLP and time series forecasting is also of interest. It's important that the work is closer to the main product of the company. REMOTE ONLY
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: fluent

- The week I joined a project that has been going on for half a year, I suggested a different approach, and it raised the R2 score from about 0.01 to 0.5 on real-world economic data.
- In a behavior prediction project helped the client to change perspective on the task, metrics and results' evaluation, which led to more insights on the dats and subsequent precision increase (10-20% for the same recall for a late-term project), and more importantly to a change of their perception of our team's success and to years of subsequent collaboration with this client.
- Implemented from scratch several cutting-egde meta-models only with basic libraries for different projetcs, and in non-poc projects they were succesfully industrialized (using only basic libraries was a client's request, I'm not that arrogant)
I have worked in a client-facing role on different projects on:
• Time series forecasting with various underlying models (neural networks, Markov and Bayesian models, standard tools like gradient boosting, linear regression etc).
• Causal inference: establishing an effect of a particular factor (way of contact a client, prices of products) on a desired outcome. In all the projects I identified and implemented the most appropriate models from research papers into shipping products.
• Reinforcement learning: drone flying in a simulation and (a personal project) optimal quantum control.
• I also participated in a non-ML international interdisciplinary research project on quantum-assisted computation. I developed architecture and a compiler for a quantum co-processor and evaluated potential computational benefits of the device.
- relocation to US/Canada (або до України: краще щось робити там, ніж сидіти тут в ЄС ніби як осторонь)
- diverse projects
- collaborative team
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 2 years | English: upper


I'm a data scientist with 2 years of hands on experience in data science and machine learning, mostly focused on NLP related tasks. 

Skills:  
-  Python 
-  PyTorch, TensorFlow, Keras, Flask 
-  Numpy, Pandas, Scikit-Learn, Matplotlib, Plotly, SciPy,  NLTK, gensim, LightGBM, XGBoost  
-  MySQL,  MongoDB 
-  GIT 
-  Docker
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 0 years | English: intermediate


I'm a graduate of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", which wants to develop in the field of Data Science. I'm interested in machine learning, data analytics and data engineering.

Tech skills:
- Programming languages: Python, C++, SQL (PostgreSQL, MySQL)
- ML & DL Libraries: NumPy, pandas, Matplotlib (seaborn), SciPy, scikit-learn, XGBoost, TensorFlow, Keras, PyTorch
- ML Algorithms: k-NN, Linear Regression, Logistic Regression, Naive Bayes Classifiers, Decision Trees, Ensembles of Decision Trees (Random Forest, Gradient Boosted Regression Trees), SVMs, K-means
- Math: Calculus, Linear algebra, Combinatorics, Probability theory, Statistics
- Tools: Docker, Linux (Bash), Git, PyCharm, Jupyter Notebook

I don’t have any experience, so I did a project. I used statistical analysis methods and machine learning algorithms to develop algorithmic trading strategies, as well as vectorized backtesting to evaluate their effectiveness. Check out my GitHub.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 2 years | English: intermediate


I’m a machine learning engineer with 2 years of experience in model development and python programming.

Experience:

February 2021 – Present
Machine Learning Engineer:
Developed ML solutions in predictive machine learning, which were used to automate various 
processes in the company. Implemented ML models to predict user behavior and different 
indicators, such as company revenue. Took part in the development of a real-time recommendation 
system and text summarization model. Along with the development of the models, I was engaged 
in building training pipelines, which included data validation, model testing, and deployment to the 
cloud.

October 2020 – December 2020
Data Science Intern | Kyivstar Big Data School 5.0:
Built a clusterization model for users based on their geolocation data and the places they visit most 
often. Developed a classification model for predicting the type of user’s transport using Spark ML, 
Spark Streaming, and Azure Databricks for model serving. Also, I was working on a model for car 
image classification based on a pre-trained ResNet model and engaged in the development of an 
NLP model for classifying user ratings based on their feedback using the LSTM model and PyTorch 
library.
I am looking for an opportunity to build a career and apply my
skills in real projects. I really enjoy working in a team and
communicating to achieve the best result.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 2 years | English: intermediate

I have written and published two research papers on genetics. In the first article, I analyzed the stress resistance of Drosophila during pharmacological inhibition of prostaglandin metabolism. In the second, an analysis of reproductive capacity and preimaginal mortality in Drosophila melanogaster after weak exposure to microwave radiation.
I was the team leader at the CompTech2021 hackathon. Our team developed an application that assist drivers in driving and parking functions, the so-called ADAS. In particular, I solved the road lanes segmentation problem. The final model was built on PyTorch and the metric dice score  = 0.939.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 1.5 years | English: fluent


Machine Learning Engineer - 
 Developed machine learning models to predict currency rates using time-series analysis, boosting and ensemble algorithms.
 Customized hyperparameter tuning and weights adjustment methods; implemented risk management and backtesting using
Backtrader package; automated data acquisition from Interactive Brokers using TWS and ib_insynс package.
 Built a classification model for churn prediction with an AUC-ROC score of 0.988 and F1-score of 0.798; сonducted experiments to
handle imbalanced target distribution by creating synthetic data using CTGAN.

Research Assistant
 Submitted paper titled “Mechanical and thermal properties of BN doped graphene: Molecular Dynamics Simulation and
Machine Learning Analysis”.
 Implemented Support Vector Regressor to model physical properties of graphene based on its molecular structure reaching an
R-squared of 0.75, 0.86, 0.91 and 0.88 for four targets.
 Built a reverse optimization algorithm to predict 200-D molecular structure of graphene based on desired values of its 4 physical
properties using Genetic Algorithm in Python.

Data Scientist
 Developed regression and time-series models for 21 macroeconomic indicators of Kazakhstan.
 Performed data analysis, data visualization and feature engineering in Python and Power BI.
 Conducted statistical tests to validate linear regression and time-series assumptions; Created an analytical report.
Data driven methods into solving real-world challenges.