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

Successfully delivered all the projects with a followed
acceptance.

Have my own publications in «Herald of Kiev Institute of
Business and Technology»» journal, connected to the
software development and management.
- NDA (Generative AI consultant, part-time, 05/2023 - Present).

OneAI (NLP Researcher, full-time, 02/2022 - 01/2023):
- In 2 weeks I successfully delivered a fast NER algorithm to customers, whereas a previous outsource team wasn't able to solve the task in 2 months, using SOTA solutions.
- Worked on the research of existing solutions and development of math-based algorithms superior to existing ones.
- Worked under the NER, clusterization projects.

SoftServe (Machine Learning Engineer, full-time, 08/2021 - 02/2022):
- Leading project and developing architecture. 
- Used GCP and AWS solutions for Intelligent document
processing (NER, Text Classification); transformers and
classic ML (like logistic regression) for prefinal predictions. 
- Using cloud for deployment and customer presentations. 
- Negotiating with customer about expectations; presenting
results. 
- Researching papers for finding a way of solving a problem.


Simporter (Data scientist, full-time, 01/2021 - 08/2021):
- Improved sentiment classification approach with a custom
developed solution (based on dependency parser).
- Worked on Sequence labeling, Named Entity Recognition, keywords extraction (rule-based and with transformers
ones), Single/Multilabel sentiment classification.
- Text data preprocessing & augmentation.


[*COMMERCIAL PROJECT WORK*]

- Participated in research paper writing that connected
to Text Similarity Search task in databases (09/2020-01/2021)

- Worked on developing NER solution for extracting
special entities from a medical data (requested by a
startup) (05/2020 - 10/2020)
- Have an opportunity of professional growth;
- Work in a well-coordinated team, where you can ask for advice or help your teammate;
- Lead projects;
- Participate in researches;
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 5 years | English: upper

- marketing based on user clustering increased the company's revenue by 10%
- built the machine learning infrastructure from scratch
- completed multiple specialization courses on Coursera
- performed clusterization of the free and paid users for further
marketing targeting purposes [umap, hdbscan, seaborn,
xgboost, fast-api, docker]
- built a predicting model which predicts whether a user will
become high spend user in the future [sklearn, imblearn]
- built a model which predicts revenue of the company for the
future period based on current metrics and trends [timeseries
analysis, sklearn]
- built a predicting model which identifies whether a user will
have a successful purchase in the future [sklearn, imblearn]
 - built a predicting model which predicts whether a user will ask
for a chargeback in the future [tensorflow, keras, fast-api,
docker]
- built A/B/n testing tool for the analytics department [scipy,
pandas]
- deployed all the models to the production (organized full
Machine Learning lifecycle)
I'm looking forward to work in an experienced team of machine learning engineers and data scientists in the product/outsource company and I strive to contribute to the company's success
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 8 years | English: fluent


Previously worked as an iOS/macOS Developer (for 6+ years).
4+ years ago my passion switched to DS, AI and ML area.

Higher education — The University of Bristol (United Kingdom).
Strong background in Econometrics & Statistics.
English — Fluent, IELTS score 8.0 / 9.0.

--------------------------------------------------------------------------

• Computer Vision
• Time Series forecasting (+ stationarization)
• Recommender Systems (Content-based / User-based / Hybrid)
• Multimodal & Multitask deep learning
• Supervised / Unsupervised / Transfer learning
• Creating custom layers, callbacks & loss functions
• Model Uncertainty & Monte Carlo simulation
• Ensemble modeling

• keras / tensorflow / fastai
• pyspark / jupyter notebook
• numpy / pandas
• scikit-learn / scipy / matplotlib
• random tree / random forest
• bootstrapping / bagging
• boosting (xgboost) / ensemble
• opencv (cv2) / PIL

• MLP / CNN / RNN / ResNet / Encoder-Decoder
• Agglomerative Hierarchical Clustering (AHC)
• Similarity measures / Cosine distance / Euclidean distance
• Hyperparameters optimization / Bayesian optimization
• Overfit / Underfit prevention (+ regularization)
• Dropout (+ MC Dropout as Bayesian Approximation)
• Data mining / Data augmentation
• Data normalization / Data standardization

• Warm Restarts
• Convolution / MaxPooling
• GlobalAveragePooling
• BatchNormalization / LayerNormalization
• TCN (Dilated Causal Convolution)
• LSTM (+ Bidirectional LSTM)
• Embedding / Reshape / Dense / Flatten
• ReduceLROnPlateau / EarlyStopping
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: upper


Project “Stenosis Detection in Internal Carotid and Vertebral Arteries based on Lumen Diameters”.
Input dataset contains records of evaluated diameters of internal carotid and vertebral arteries (each left and right pair) measured before and after some treatment procedures, together with the medical diagnosis. Wavelet-based approach for automatic stenosis detection was proposed. Prototype was coded in Matlab.

Project "Detection of particular geo-signal".
Processing and analyzing of geomagnetic signals for feature detection. Developing methods for signal processing and feature detection. Programming of proposed methods in Matlab.

Financial startup
Developing methods for analyzing of finance data (clustering and data mining). Developing recommendation system in finance area.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: upper


Python, R
+ Interesting and challenging tasks. 
+ Product company. 

- Python developer
- Big company
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 4 years | English: fluent

Projects and domain: time series forecast, virtual assistants, churn/retantion analysis, objects and text recognition on image.
Previous experience as a software developer.
Product owning/team leading and developing (data science, statistics): from scatch/concept/MVP to product.
Python, SQL, C++/C#.
Math/Statictics/Algo, classic and deep machine learning.
Data fetching/mining, processing/preparation and features engineering, training and validation, deployment and API development.
NLP (information retrievel systems, chatbots/virual assistents, images recognition/detection, time series prediction, churn analysis, user behavior.
Clear system for recognition and evaluation of work performance.
Adequate level of bureaucracy.
Interesting domain.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 5 years | English: fluent

IBM Select Projects:
1/ Ising model for financial assets pricing – Spring 2019
2/ Semiclassical Shor’s factoring algorithm (FFT) – Winter 2019
3/ Quantum SVM discriminative classifier – Winter 2019
4/ True random number generator algorithm for RSA encryption – Fall 2018

ML Select Projects:
1/ Designing classifiers for discovery of mispriced securities (SVM, PCA, Knn)
2/ Deploying TNC, CG, Lagrangian for convex optimization problems in the computational investing domain.
Q3 2018 - Q2 2019
IBM
// Subject matter expert of IBM Q global team focusing on developing quantum computing applied solutions for ML, data science

Q2 2018 - Q3 2018
Canadian fintech startup
// ML-based software for Computational investing. Discovery of under- and overvalued securities via equity valuation, industry-specific metrics

Q2 2018
YahooFinance 
// Python-SQL based computational investing solution for web data extraction and analysis

Q4 2017 - Q2 2018
FRM Consulting
// setting-up quantitative risk management system for banks and mutual funds based on FINCAD API for Python

Q2 2015 - Q3 2017
MBA 
// University of Alberta, University of Mannheim
I am Interested in joining challenging, fast-paced environment with focus on high-tech stack IT solutions in Data science, ML.

The Big Data, blockchain and all kinds of web development are out of scope of my professional expertise.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 6 years | English: fluent

Led several projects in the field of genomic analysis, some required implementing scientific papers from scratch, and each of them reached the clinics and is now used by some of the world's largest labs and hospitals.

Automated lunch ordering for my team with web scraping and Google spreadsheets :)

Wrote a bachelor's thesis on the application of clustering in political science, and there were some interesting results)
R&D engineer with 6 years of commercial experience in startups of various stages: from pre-seed to acquisition.

Worked in healthcrae, transportation and fintech domains on roles that involved 
* software engineering (from scratch and in legacy codebases; Python as the main language),
* data engineering (mostly in-house pipelines and AWS),
* data analysis and machine learning (mostly tabular and graph data commercially, self-studied image and text domains).

Familiar and comfortable with startup environment and distributed cross-functional teams, communicating results to non-technical stakeholder, being part of research projects, working in regulated domains.
My perfect job is one where I can learn new things and create useful products.

Not interested in pure web development roles.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 2 years | English: fluent

Conference Open Readings March 2019, Vilnium Lithuania: 
"The contribution is recognized as one of the best presentations on Life and Natural Sciences in 62nd International Conferences for Students of Physics and Natural Sciences "Open Readings 2019" "
--- Statement ---
Looking for a position of Middle Data Scientist/Machine Learning Engineer. Interested in Computer Vision, but in general would be glad to work with any type of data and tasks.

--- Responsibilities & Tasks ---
[Data Scientist 2 years]
R&D department of Sierentz Global Merchants
- Research, testing, assessment and development of machine learning solutions for geospatial clustering (Birch algorithm), predictive crop progress modeling (1D CNN) and predictive yield modeling (ensembles of RandomForest)
- Creation of machine learning systems infrastructure from scratch
- Evaluation and validation of new and existing models
- Exploratory data analysis & mining of geospatial and time series data (e.g. remote sensing data, crop progress data)
- Data scrapping, preprocessing, validation and visualization
- Feature engineering and dimensionality reduction 
- Development and code implementation of algorithms for extraction of extra information from time series data
- Report creation and presentation
- Management of Junior Data Scientists & Junior Machine Learning Engineers 

Also previously
[Research Assistant 3.5 years]
National Taras Shevchenko University of Kyiv
- Design, conduction and management of neuroscientific research projects
- Data collection, preprocessing and analysis
- Development and testing of research hypotheses
- Literature search, analysis & interpretation of research results
- Creation of reports and presentation of research results (poster & oral presentations)

--- Stack Overview ---
Main domain:
Machine Learning, Data Science, Probability & Statistics, Statistical data analysis 

Aux domain: 
Biology, Neuroscience, Research design 

Main stack:
Python, Git, Linux, Bash, PyTorch, TensorFlow, sklearn, numpy, pandas (+ geopandas), matplotlib, scipy, xarray, Open Data Cube, OpenCV

A bit familiar with: 
C, TFX, GoogleCloud, FastAPI

--- Side projects ---
- Basic solution for Image Colorization implemented in PyTorch 
Architecture: modified U-Net (ReLU changed to Leaky ReLU)
Datasets: CIFAR-10 + Dogs vs Cats
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 5 years | English: fluent

Google Tensorflow Certified data scientist with 5+ years of experience
5 peer-reviewed publications
Two times finalist at International Data Analysis Olympiads, obtaining 16th place among 2187 teams.
Tensorflow Certified data scientist with 5 peer-reviewed publications and proven practical experience in machine learning, natural language processing, predictive modeling, and python. Two times finalist at International Data Analysis Olympiads, obtaining 16th place among 2187 teams.Looking to use my experience to build machine learning and data-related solutions.

Senior Data Scientist / Machine Learning Engineer
( 5 years )
-Generated word embeddings and applied them to sentiment analysis. Handled data collection, parsing, text preprocessing, model building, and model evaluation steps. Obtained 85% accuracy score on extrinsic evaluation task. Models: word2vec, CBOW, skip-gram, GLoVe. Stack: gensim, Tensorflow, Keras, Matplotlib, seaborn, NLTK, spaCy, fastText.
-Built a machine learning model to predict customer waiting time in the queue leading to a reduction of 26% in queue-related complaints. Stack: Pandas, NumPy, XGBoost, CatBoost, LightGBM, Docker, matplotlib.
-Wrote language-specific text analyzers in Elasticsearch. Improving the search functionality of our websites. Stack: Elasticsearch, Pandas, SQL, Tableau. 
-Developed sentiment analysis model for measuring user satisfaction which reduced 12 hours per week of manual labeling and analysis time. Models: SVM, LSTM, RNN, BERT. Stack: Tensorflow, Keras, scikit-learn, SQL, Tableau, Pandas.
-Trained an RNN for generating text based on the works of outstanding poets.
Trained custom named entity recognition system. Stack: LSTM, sklearn-crfsuite, spaCy, NLTK, eli5.
-Worked on the text classification and automated analysis of news articles which involved training machine learning algorithms for classification and analysis. Built data preprocessing, vectorization, and feature engineering pipelines. Trained various machine learning / deep learning models for text classification and keyword extraction. Implemented probability calibration and feature reduction for performance improvements. Stack: MongoDB, Tensorflow, Keras, scikit-learn, SQL, Tableau, Pandas. Fast-API.
Responsible for the full machine learning lifecycle: predictive models, development, new ideas inducing, proof of concepts, implementation in production environment, monitoring.
I am a team player. Therefore I love big projects and well-established processes.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 4 years | English: intermediate


Text classification models.
Named Entity Recognition.
Topic modeling. 
Server-side programming.
Migrate monolith to microservices.
Working directly with customers to understand business needs and provide solutions.
Writing automated tests for code.
Web scraping and parsing.
Project support.
Providing technical training for the team.
Presenting internal and external demo.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: upper

I learned everything on my own thanks to self-motivation.
I have proven to colleagues that using deep neural networks can be useful and improve results even on small datasets in such specific domains like brain computer interface domain.
I completed 16 online courses and won 3 European research grants.
The project is focused on methods for on-line detection of neural rhythm changes in the motor system with application to brain-computer interfaces.

Responsibilities: 
Application of machine learning and deep learning techniques in different brain-computer interfaces using Python’s Scikit-learn, Keras,  Tensorflow -- research papers reading, deep neural networks implementation, modification, dataset preparation, EDA, networks training;
Time series analysis to assess the depth of anesthesia using brain-computer interfaces (filtration, spectral analysis, time-scale analysis using Python);
Detection and classification of motor imageries in EEG using Python’s Scikit-learn, Keras,  Tensorflow;
Riemannian geometry application for feature extraction and classification in motor imagery based brain-computer interfaces using Python’s pyRiemann;
Implementation of anomaly detection for BCIs using Python’s Scikit-learn;
Spatial brain activity visualization using Python’s Matplotlib, Seaborn, MNE;
Data analysis, statistical calculation and visualization using Python’s Pandas, NumPy, SciPy;
I want to work in a team and learn from more experienced colleagues. I was the only data engineer in the team at my last place. 
I want to share and gain experience. For me product company would be a more preferable choice. But outsource would be also good.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 0.5 years | English: upper

Vodafone Music Hackathon (10th place / 57)
Dec. 2018
Predict which of the users of the telecom operator Vodafone will use the app Vodafone Music.

Advanced Machine Learning and Signal Processing - eDx
Mar. 2019 - PRESENT

Data Analysis with Python - Cognitive Class
Nov. 2018

Machine learning and data analysis - Coursera
Mar. 2018 - Sep. 2018

Master degree, Nuclear Power Engineering, Graduation with
honours (GPA 4.99 / 5.0)
National Technical University of Ukraine "Igor Sikorsky
Kyiv Polytechnic Institute"
Work experience:
December 2018 - April 2019 data scientist in uData School
● Designed and implemented Machine Learning models for forecasting and clustering;
● Performed BI analysis with sources up to 100 GB to identify fraudulent activity and trends;
● Designed slides for presentations with proposals in consultancy projects.

Projects:
● CPV predictor for Prozorro (a public e-procurement system)
The model determines which category (CPV code) of goods
(services) the tender belongs to. Tender description used as an
input.
● BI analysis for Prozorro
Visual analytics on public procurement.
● Cryptocurrency price prediction
● Interpreter (educational project)

September 2018 - December 2018 trainee in uData School
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: pre


Work Experience:
• speech-to-text, text-to-speech
• image classification, image segmentation                                                                                      
• data processing
• deploying and maintaining data annotation tool

Skills and abilities:
•	Neural Network and deep learning(CNN, RNN, autoencoder)
•	Computer vision, classification, object recognition
•       OOP, algorithms and data structures
•	Linear algebra, probability theory, discrete mathematics, statistics
•	Python
•	pytorch, numpy,  skikit-learn, pandas 
•       Linux
•	GIT
•	Ukrainian(native), Russian, English(Intermediate)
Work in a strong team, create advanced products.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 5 years | English: fluent


Work experience :  Total 5 years work experience
3 years of Python programming background and 2 years of Machine learning experience
Python Instructor at ALAS Academy
Computer vision engineer at QSS Analytics 
Python Instructor at Data Science Academy
Python mentor at Data Science Academy (QSS Analytics) 
Big data mentor at Data Science Academy 
Data Science  at QSS Analytics
------------------------------------------
Projects
-------------------------------------------
Traffic control system with Computer Vision
Bad Habit detection for children with Autism disorder
Facial recognition app with Python
Object tracking with Polynomial Regression
Position: Data Scientist | Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: upper

• Bachelor's degree in Mathematics.
• Master's degree in Applied Mathematics with specialization in Machine Learning.
Data Scientist:

Company A (Fintech)
• Experience - 1 year 6 months.
• Responsible for initial and repeat loans in Vietnam.
• Development of the scorecard (Machine Learning Model). Developed the scorecards and deployed them into production.
• Extracted insights from datasets to drive strategic decision-making and optimize business outcomes.
• A/B testing.
• Fraud Analysis. Deployed into production. Tested the effectiveness of Anti-Fraud rules.
• Backtesting using data from new sources.
• Monitored main metrics.

Company B (Outsource)
• Experience - 1 year 6 months.
• Data Parsing by using requests, selenium, and beatifulsoup packages.
• Time Series Forecasting. Trained 100+ time series by using fbprophet package.
• RFM Analysis. Applied standard segmentations & k-means clustering model.
• Interns mentorship.


Math teacher:

Company C (University)
• Experience - 1 year.
• Lecturer of math courses: Calculus, Discrete Mathematics.
• Prepared and conducted midterm and final exams.
• Taught over 600 students.
• Competitive salary.
• Work-life balance.
• Want to learn more about Natural Language Processes (NLP), Computer Vision (CV), Recommendation Systems, and Time-Series.
• Open to learning new tech stack.
Position: Data Scientist | Machine Learning Engineer
Category: Data Science
Experience: 5 years | English: upper

- M.SC computer science, Thesis on Automatic wordnet construction using sense embedding for local language

 - finished  data science and visualization training offered by UN Analytics & Visualization Team, 6 months of rigorous training.
Developed machine learning solutions in various contexts related to Computer vision, Tabular data, Time series, Digital signals processing, and more. Experienced in building efficient Machine learning pipelines and overall MLOps strategy, moving within the unclear scope of requirements, taking a leadership role, communicating with C-level stakeholders.

Last projects:
Development of Chatbot for health care assistance using DialoGPT, Sbert for sentence similarity, BiLSTM+CNN for predictive modeling



Machine Learning, PyTorch, Deep Learning, Data Science, Git, computer vision, Docker, Python, Pandas, SQL, numpy, scikit-learn, Jupyter Notebook, statistics, matplotlib, PySpark, Databricks, AWS S3, AWS Sagemaker, MS Azure ML Studio