lang-uk/recruitment-dataset-candidate-profiles-english huggingface dataset:
Position: Data Scientist, Machine Learning, Data Engineering
Category: Python
Experience: 1 years | English: fluent

Got a chance to be an intern at Google this year, helping to solve a challenging but important problem with abuse fighting.
Awarded a partial​ scholarship during the first year at university based on previous achievements.
Top 3%​ among U​krainian students ​based on entrance tests results (199/200).
Work Experience:

Summer Trainee Engineering Program Intern at Google, Zürich, Switzerland (July – October 2022)
As part of the Privacy, Safety, and Security team, developed a visualization tool that tracks abusive accounts and provides visualizations of their actions and characteristics. This tool is used by analysts to simplify their day-to-day work, resulting in increased quality of the abuse detection system in the long run.
Conducted research of potential solutions for the problem, as well as technical aspects, which led to results being of high quality.
Technologies Used: Google development stack, Python, Pandas, GoogleSQL, HTML, Typescript & Vega.



Speaking about my projects:

Image Stitching (February – June 2022)
Used Python, OOP & Linear Algebra Principles to implement an algorithm that composites multiple overlapping images captured from different viewing positions into one natural-looking panorama. 
Implemented from scratch horizontal and vertical stitching for several images using the K-Nearest-Neighbors algorithm & David Lowe's ratio test for Feature Matching, Homography estimation with Ransac algorithm, and SIFT algorithm for keypoint detection.

Sportify, Project Manager (February – June 2022)
Managed a team of 7 students. Performing competitive analysis and market research, defining a unique selling point (USP), and creating a strategy and implementation plan. Developed high-quality benchmarking, technical documentation, and mockup using Figma, resulting in an easy-to-use marketplace for coaches and sports clubs' clients, which increased demand for sports activities by 23%, according to a survey of coaches and gyms.
 
Spam Classifier (October 2021)
Used Python, Pandas, NLP principles, OOP & Multinomial Naive Bayes algorithm to develop a spam filter with 96% accuracy. 

Musical Telegram (April – May 2021)
Used Python & correlation ML model to develop a Telegram bot with Spotify API integration that simplified finding new music based on preferences for 300+ students.

Friends on Map, February 2021
Used Python & HTML to create a web application for friends’ locations map generation of any Twitter account by incorporating Twitter API and Flask framework.

I am an active volunteer with broad experience organizing and assisting in major events. Speaking about some of them:
Designer & Website developer at TEDxUCU
Organizer of Apps Summer Camp
Tech-Supporter for Lviv Data Science School
I would like to work in an open team where you can develop and gain experience from each other.
Passionate about gaining more knowledge and experience in Data Science, Python engineering, Computer Vision, Artificial Intelligence, and NLP.
Position: Data Scientist & Machine Learning & Deep Learning
Category: Data Science
Experience: 1.5 years | English: fluent

Almost all my experience is related to health data analysis and bioinformatics. 
Currently, I am involved in a computational neuroscience research project. Anyways, during the last three years, I 've been working with lots of other projects and actively using my data science skills.
My interests include Natural Language Processing, Text Mining, and all of the fantastic Machine Learning stuff. Again, I am not about words in my technology stack - I am about analysis. Without a deep understanding of the domain part - successful data mining is impossible. I believe, this understanding my main achievement. 
If you want to chat about future perspective, please do not hesitate to contact me.
NLP project for medical articles classification. We used Python libraries to classify medical papers based on cancer severity discussed there. We trained SVM to solve that problem and used TextBlob and NLTK for data processing. 

ProstateX project, to classify MRI images based on four imaging modalities and figure out whether a specific patient has a malignant tumor or not. We used a wide variety of technologies and stick with Python packages for CV and medical image processing, such as SimpleITK, Skimage, and openCV, etc. We trained VGG16 classifier and used Keras in this project. 

BraTS project. In this project, we trained a deep neural network to automate intratumor segmentation. Hence, our system was able to segment the tumor into four different zones/classes. We used libraries mentioned above for image processing and pre-trained VGG-16 to solve this problem. Furthermore, I received great results. 

Meanwhile, I am working on analyzing magnetoencephalography data and doing sophisticated mathematical modeling to determine coherence among brain regions. Mostly, I use Matlab and Python and their packages and libraries for signal processing and data visualization.
I am interested in Computer Vision and AI projects, which might have a tremendous social impact. I want to work with great people and solving different sophisticated tasks. These tasks may include, but not limited to medical image analysis, image analysis/processing, computer vision, signal processing, deep learning applications, machine learning applications, etc. I am interested in Data Science and industry applications as well.
Position: Data Scientist / Machine Learning / Deep Learning
Category: Data Science
Experience: 0 years | English: upper

Currently on 2-nd year earning Bachelors degree in Applied Mathematics
Developed Many projects with variety of different programming languages and technologies
Two years of univesity studying for Applied Mathematics
Course projects in C, Python and Java
Developed a telegram bot for university applicants for student council
Online courses on Python development, Machine Learning and Data Science
To work and grow as Data Scientist / Machine Learning Engineer
Position: Data Scientist / Machine learning / Deep learning engineer
Category: Data Science
Experience: 2 years | English: upper

I used to work with projects from scratch with much domain research involved. So I can state the problem clearly, finding the optimal solutions and implement them quickly. Also, after working with unstructured and noisy data I know how to find relevant predictors and clean unnecessary ones. 

In the betting project, we increased the revenue by 20% compared to the traditional approach, starting the project from scratch with no domain knowledge to help.

In the micro-credit scoring project, I found relevant variables and constructed the machine learning model which predictions helped the main model to estimate loaners with no credit history.

Not all my findings happened to be successful, but I am always trying to get all from what I have.
I have experience in developing predictive and modeling systems with the use of machine learning. These are projects I have worked on:
1. Credit scoring for micro-credit loaners using mobile data. I was responsible for discovering significant predictors, providing analytical insights and constructing the machine learning model for improving main model scoring. This task involved dealing with noise and testing many hypotheses. We also used open-source data together with extensive scraping to complement our internal sources. In the end, we discovered significant features which boosted our traditional scoring.

2. The development of an automatic horse racing betting system. Made a multilayer model for predicting the winner in a horse race and following budget allocation system. Increased revenue by more than 20% compared to the traditional approach.

3. Computer vision project. My pet-project that I am actively working on now. It is about using GANs for removing the shadows from images. I have conducted extensive experimenting in the domain and gained much experience in computer vision with PyTorch.

I have much experience with machine learning modelling, working on analytical projects and dealing with unstructured, noisy data.
I am passionate about working on interesting projects, especially computer vision/deep learning-oriented.

Have strong skills with Python and DS packages(pandas, numpy, scipy), machine learning(scikit-learn, xgboost, lightgbm), deep learning frameworks(PyTorch, TensorFlow).
Position: Data Scientist Machine Learning/Deep Learning Engineer
Category: Data Science
Experience: 2.5 years | English: fluent


SmartKiwi AI - Full Stack Data Scientist:
Led spatiotemporal demand prediction models for shared mobility, boosting profitability by 15%. Developed MLOps system, reducing deployment time by 30%. Collaborated on dynamic pricing algorithms for a 12% revenue increase. Aim to optimize dynamic pricing and further explore advanced demand prediction.

Affable AI - Data Scientist:
Reduced sentiment analysis response time from 180 to 23 seconds. Implemented NLP for 25% improved brand classification. Optimized Deepface for ethnicity classification. Ongoing sentiment model refinement and advanced NLP utilization.

University of College London - Machine Learning Researcher:
Improved heat furnace control by 200% using the data-driven solution. Enhanced reliability with an automated data pipeline. Enhanced 12 industrial data frameworks' quality assessment. Seek further heat control optimization and real-time data integration.

University of Houston - Computer Vision Researcher:
50% faster image annotation via Python, PyTorch, and OpenCV. 20% faster face recognition via a Siamese network. Optimized data preprocessing for training efficiency. Aim for increased face recognition accuracy and faster annotation.
Position: Data Scientist, Machine Learning Dev
Category: Python
Experience: 4 years | English: upper

- ML skills 
- Improved speed of synchronization between iOS client and server by 6 times for fire protection project
- Manage a team of 11 devs
- Data Science, ML, Python Developer
- Senior iOS Developer
- Team management of 11 devs
- Pre-Sale experience
- Project initiating experience
- Long term projects
Position: Data scientist, Machine Learning engineer
Category: Data Science
Experience: 5 years | English: fluent


Data Scientist with 5+ years of working experience in Data Science and Machine Learning .

Interested in a high-level position. Passionate about data driven decision making, product analytics. Skilled in developing segmentation and recommendation systems, classification tasks.

For a last year leading a Data Science team.
Also responsible for the end-to-end development process including presenting and describing results for non-technical managers.
Professional growth. Leading the team.
Position: Data scientist / Machine Learning Engineer
Category: Data Science
Experience: 3 years | English: upper


3+ years of practical experience in advanced analytics, NLP, text-mining, time-series analysis and forecasting.
NOT data engineering
Position: Data scientist / Machine Learning Engineer
Category: Data Science
Experience: 5 years | English: upper


Monte-Carlo simulations in insurance, Ensemble methods for sales forecasting, Deep reinforcement learning for Black-Scholes hedging. Opencv+YOLO pedestrian counting using a webcam. Clustering methods for determining types of aircraft engines.

Deep Learning in Roofing. Usage of CNNs for inner rooftops geometry segmentation and polygonization and pitch detection.

....
It doesn't matter actually. I'm open for any proposals for my review.
Position: Data Scientist / Machine learning engineer
Category: Data Science
Experience: 3 years | English: upper


1 year of experience in fintech, worked on researching , developing machine learning models
2+ years of experience in developing recomendation systems

Excellent mathematical skills
Well-developed system thinking and logical reasoning
Concepts: Data Analysis, Machine Learning, Object-Oriented Programming, Deep Learning
Programming Languages (Advanced Knowledge): Python
RDBMS: SQL Server, MySql, Snowflake
Programming Languages (Basic Knowledge): R, C++, Java
Deep Learning libraries: Keras, TensorFlow, Pytorch
DS libraries: Scikit-learn, pandas, numpy, scipy, lightgbm, catboost etc.
Cloud: AWS
DevOps: Docker
VCS: Git
Position: Data Scientist / Machine learning engineer
Category: Data Science
Experience: 8 years | English: upper


- Data Scientist (~3 years)

Projects: Antifraud model for mobile banking, User engagement, Churn prevention

Stack of technologies: Python, C#, Java, AWS (S3, RDS, Comprehend, QuickSight, Athena, EMR...)

Product company in fintech with stockholder in USA, only remote. Built models for fraud prevention using Machine Learning algorithms (if we have enough fraud session history, Neural Network classify sessions), linear algebra (distance between current user vector and typical vector) from scratch to prod ready. Product design, prototyping, development of all the software used in this project

- Data Scientist (~1,5 year)

Projects: mobile app in "health and care", prediction of viral content in social media (regression, trend detection, k-means, arima)

Stack of technologies: Python (TensorFlow, Keras), R, Tableau. Sql: redshift,vertica, mysql . GA, appsflyer, Apple store api

As data analyst I develop from scratch: DWH scheme, system of reporting, A/B testing, collect data from different source, user behavior analysis. 

- Data Scientist (~1 year)

As a part of "Data discovery team" I took a part in data R&D, looking for insights which may reduce fraud, chargeback rate, increase profit.

Stack of technologies:- R (devtools, networkD3, sqldf, ggplot, e1071...) - Python (scikit-learn, Pandas, NumPy...)- SQL (Vertica)- Tableau
Looking for longterm cooperation
Position: Data Scientist / Machine learning engineer
Category: Data Science
Experience: 1 years | English: intermediate

o	Developing a system for traffic profit assessment
o	Designing a recommendation system for matching users on product
o	Develop CTR predict ML models
•	Experience in Machine Learning and Deep Learning;
•	Participant in competitions on Kaggle platform;
•	Understanding machine learning and deep learning algorithms;
•	Proficiency in math, statistics, and probability theory;
•	Using data science tools and machine learning algorithms in building materials research work;
•	Experience in developing robots for arbitrage trading in cryptocurrency markets using python;
•	More than 4 years experience in Civil Engineering as a project manager.
Position: Data Scientist / Machine learning engineer
Category: Data Science
Experience: 4 years | English: fluent


I had a 2.5 years experience in Machine Learning, Computer vision and Data science.
Problems that I had my hand on:
- Unsupervised segmentation
- Object detection
- Object segmentation, instance segmentation
- Optimizers for diffrenet data related tasks
- All kinds of regression/classification tasks

My core languages are Python and C++. 
I have an experience with both Tensorflow and PyTorch and other DS/ML libs - numpy, pandas and etc. 

Got my hands on both relational and not relational databases:
mysql, postgre, sqlite, mongodb, cassandra.
Know Git version controling system.
Finished Computer science BS at Ukrainian Catholic University
I'm mostly interested in machine learning, though I'd gladly consider all of the offers.
Position: Data Scientist / Machine learning engineer
Category: Data Science
Experience: 1 years | English: upper

Participant and prize-winner of the Ukrainian Final and participant of the European Final of the International Collegiate Programming Contests (ICPC), an international student team programming competition.
200+ solved problems on leetcode on algorithms and data structures.
Codecademy Python intermidiate & advanced level certificate
Projects:

Face emotion detection and object detection using Resnet, FastRCNN and OpenCV Cascade Classifier
AI that plays snake game using PyGame, RL techniques and CNN
Picture style transfer using pretrained VGG layers
Text generation in the style of Taras Shevchenko using Transformer
Ukrainian news classification by topic using classical ML algorithms such as Naivie Bayes, Logistic Regression and Random Forest Classifier

Skills:
Programming: Python, C++
ML frameworks:  sklearn, pandas, NumPy, matplotlib, XGBoost
DL frameworks: PyTorch, OpenCV, NLTK
ML algorithms: kNN, Linear & Logistic Regression, Decision Trees, Random Forest
DL algorithms: Neural Network, RNN, CNN, Transformers, ResNet
Math:  Statistics, probability theory, linear algebra
Algorithms and data structures
Other: SQL, Git, A/B test
B2 English, B1 Polish and A1 Japanese
Position: Data Scientist, Machine Learning engineer
Category: Data Science
Experience: 1.5 years | English: fluent


- Python (Pandas, Scikit-learn, Tensorflow, keras)
- R (dplyr, ggplot)
- Machine Learning (Linear Models, Classification, Clustering, Ranking, Dimensions Reduction, Anomaly Detection, time series forecasting with fbprophet, basic text mining)
- Data Visualization in Python(Seaborn, Pyplot, Plotly), R(ggplot2)
- Mathematics, Probability Theory, Statistics
- Rich experience in various reporting models development
Interesting data science projects with the focus on math & stats. Full-time but remotely.
Position: Data Scientist/Machine Learning engineer
Category: Python
Experience: 4 years | English: intermediate


Data Science/Machine Learning Engineer at LLP "Alem Research", IT Company
Period: 06/2019 - 12/2021
- Designed, implemented and evaluated ML model to predict depression on social media posts using screening user's profile. Result: 66 teenagers with mental problems agreed to get professional help.
- Managed a process of classification complaint messages from social networks and media resources.
- Improve ML model to determined complaints on kazakh language from 53% to 78%(F1 score).
- Had re-trained ML model, using DeepPavlov, on russian and kazakh languages to identify NER on local area. Decreased to 30% per week manager's work to label posts.
- Developed recommendation process for managers to label social media posts by 15 areas based on ML model. New recommendation processes led to a 14% increase in managers productivity.
- Composed production-grade code to convert machine learning models into services and pipelines to be consumed at web-scale.

Data Science/Machine Learning Engineer at "Kaspi.kz", FinTech Company
Period: 12/2021 - Present
- Implemented the service to analyze and monitor conversation between manager and client using ML model and specific strict conversation rules which need to be abidance. Increased worst managers efficiency from 21% to 26 in 1 month, which helped to save company 197 million NPL.
- Led new metrics for evaluating the performance of managers.
- Researched, designed and implemented machine learning applications to solve business problems affecting more than 5 million of users, processing +100k calls per month.
- Implemented the model deployment process for the speech analytics team. New process allowed to reduce the deployment time by three times.
I want to improve my knowledge on deep learning, develop auto-learning ML models, learn about other deploy method, more coding.

I do not want to be data analytic.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 0 years | English: upper


19/07/2019 – CURRENT – Minsk, Belarus
ITGENIO--Tutor of programming on the remote.
Responsibilities:
-To teach students the basics of programming Python and Scratch.
-Communicate with students, their parents, and colleagues.

I usually learn something new, recently I have studied the Basics of DS and ML and now am looking for a full-time or part-time job as Machine Learning Engineer or Data Scientist.
I am a quite curious person, so I usually study some new skills. Earlier I studied the principles of data science(DS tools to extract, process, explore, and analyze datasets) and the basics of data preparation (data mining, cleaning, and transforming ). Later I studied the usage of Machine Learning methods & algorithms for train models to solve different kinds of problems. In my next work, I want to implement all my new skills and Math and Statistics background to work with real projects and gain commercial experience.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 0.5 years | English: intermediate

1. I use my mathematical skills in finance analysis. For example: found most profitable strategy in model with different prices, volumes and cost producing.
2. Write online stock prices to PostgreSQL
3. I try to use ML and help to earn in stock trading.
4. Transfer DB from MS SQL to 1C ERP, also in online mode.
1. Data Analyst in food producing company;
2. Development and implementation 1C for different companies;
3. Using ML for stock trading;
1. I want to develop skills in ML.
2. I want to create trading system using ML.
3. I want speak fluently in English.
Position: Data Scientist / Machine Learning Engineer
Category: Data Science
Experience: 1.5 years | English: upper

Int20H 2021 Online Hackathon 3st place in the Data Science category
1) Machine Learning BaseCamp(February - June 2021)

2) Diploma work: Тhe most influential information ("words" in google
searching) were highlight. Using ML models (Linear Regression,
XGboost, Random Forest, Extra Trees for non stationary time series),
exponential weighed average, weighted average, lags. Bitcoin
prediction for 1 day.

3)  Binary classification problem:
Exploratory data analysis. Comparing dimensionality reduction with PCA(choosin different variation value), LDA, correlation range(Pearson and Spearman) and comparing. Balanced data set(removing from the sample). Working with outliers(define and delete). Comparing results from trained models. 

4) Object recognition course at university
Supervised multilabel classification. Data augmentation. Training models using ML and DL.

5) Time series analysis using math model and comparing their (AR, ARIMA, ARCH, GARCH models).