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

- Successfully led a team of 3 to create three end-to-end products, from ideation to completion.
- Made significant contributions to building two startups, participating in the entire product development lifecycle from idea conception to finished product.
- Authored a paper that was presented at an international conference in the field of computer vision.
- Bachelor degree in Applied Mathematics and Master degree in Data Science.
ML Engineer with a specialization in Computer Vision and additional expertise in NLP. With a proven track record of working in both research and commercial development for 5 years, including managing a team of 3 developers. Adaptable and flexible in a fast-paced startup environment, with a passion for leveraging AI to solve complex problems.
Position: Machine Learning Engineer | Computer Vision Engineer | Data Scientist | NLP Engineer
Category: Data Science
Experience: 3 years | English: fluent


As a Data Scientist with more than 3 years of experience, I have worked on a variety of projects, primarily focused on NLP and computer vision tasks.



I am one of founders of AzCorpus who are building a language corpus for underrepresented languages for fine-tuning LLMs
Position: Machine Learning Engineer (Computer Vision) | MLOps
Category: Data Science
Experience: 4 years | English: fluent


Have been developing recommendation algorithms at TikTok.

----------

Before was responsible for the Faces Analytics department and led a team of 5 engineers

As ML part of my job I was responsible for 2 areas of research: Faces Detection & Keypoints Estimation and Faces Attributes recognition where held the whole ML Cycle. Participated in Face Recognition model training and Spoofing identification.

As SE I was responsible for Vide Face Analytic module written in C++. The module took an FHD video stream as input and performed Faces Detection, Tracking, Recognition, Attributes Recognition (Age, Mask, BEard, ...), Spoofing Detection, Re-ID, and Quality estimation for both Faces and Frames. The whole pipeline worked on a few CPU cores in real-time.

----------

Before
Developed and deployed real-time DL model for Human Body Pose Estimation on a mobile phone.
Developed ML algorithm to imitate personal trainer.

----------

And Early in my career
Developed  algorithms for Video/Audio segmentation and clustering. Anomalies search in Videos. Query Search in Video and Audio.

------- SKILLS --------

• Languages: : Python, C++ (STL), Bash, JavaScript
• Technologies: : CNN, LSTM, GANs, Transformers, GNNs, Reinforcement Learning
• ML/DL tools: : PyTorch, Lightning, Scikit-Learn, OpenCV, DVC, Hydra, ONNX, OpenVino, W&B, MLflow, TensorBoard
• Data manipulation/visualization tools: : NumPy, Pandas, Matplotlib, Seaborn, NetworkX, Folium
• DevOps: : Docker, Docker-Compose, Git, Gitlab CI/CD, Nginx
• Other: : PyQt, React, HTML, CSS, Unreal Engine, LATEX
Position: Machine Learning Engineer, Computer Vision, Python Developer
Category: Python
Experience: 3 years | English: fluent


# Personal Projects
->Beyond Correlation: Causal Inference
       A causal inference analysis for breast cancer by building causal graphs and an inference engine
->Sales prediction with time series data
->A/B testing with Machine Learning
->Twitter data analysis
->Telecom customer data analysis for identifying improvement areas
->A speech-to-text engine for two African languages

# Work experience
-> Built large-scale, fast, and resilient scrapers and ETL pipelines.
-> Optimization and maintenance of containerized services.
-> Built containerized microservice-based backend systems for a SaaS system.
-> Built a price prediction model for volatile assets.
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Position: Machine Learning Engineer [CV]
Category: Data Science
Experience: 3 years | English: upper

1. Product data processing pipeline creation. Detectors, segmentation models integration, postprocessing. Interesting CV algorithms for image processing. 
2. Team leading, project owning without much experience.
**Tech Lead**
- Developed process pipeline for the solution. Integration and unit test writing. CI/CD GitHub actions.
- Computer vision algorithms development and implementation. NN-models integration and post-processing. 
- Dockerisation for pipeline parts. 
- Organised department's successful work in the organisational structure change. Project owning on every step. Business/Engineering balance.
- Product integration.
- **Buzzwords**: Neural networks, Testing, Integration Tests, Python, Computer Vision, OpenCV, Docker, AWS, 3d basics.
-------------------------------------------------------------------------------
**Software Engineer**
- Neural networks in the browser. Tensorflow.js. Valila javascript.
- Model converting and serving. Building the validation pipeline based on the model output. Metric gathering.
- Computer vision application for Apparel Industry.
- **Buzzwords**: Neural networks on the Edge, Testing, Integration Tests, Python, JavaScript, Tensorflow.js, Computer Vision, OpenCV.
-------------------------------------------------------------------------------
**Team Lead**
- Building the engineering group from scratch. The organisation of advertisement, interviewing, onboarding, contracts, instructions, equipment, etc. 
- Mentoring, knowledge building. Algorithms, best practices, etc.
-------------------------------------------------------------------------------
**Software Engineer**. Computer vision system for mechanical systems automatisation.
- R&D with industrial cameras for automation. 
- NN for defect detection in real-time. Keras and TensorFlow. YOLOv4, YOLOv4 tiny.
- Built Opencv with GPU so It can accelerate the inference.
- Dataset creation. Annotation, cleaning. 
- **Buzzwords**: Computer Vision [CV], OpenCV, Neural Networks[NN, DNN], Python, Data gathering and preparation, LabelImage, Data processing, Data augmentation, Keras, Tensorflow, YOLOv4.
-------------------------------------------------------------------------------
**Software Engineer**. **Additional contract**. Product POC.
- Landmark detector training. YOLOv4 tiny, config tuning, anchors reconfiguration.
- Aggregating training dataset from existing and annotation of new data. Unification of the annotation. 
- Landmark detector demo for clients' usage. Video feed processing.
- **Buzzwords**: Computer Vision [CV], OpenCV, Neural Networks[NN, DNN], Python, Data gathering and preparation, LabelImage, Data processing, Data augmentation, Keras, Tensorflow, YOLOv4.
Adequate salary, stable workflow and processes.
Strong team. R&D will be a plus.
Position: Machine Learning Engineer (CV, NLP)
Category: Data Science
Experience: 3 years | English: fluent

Check my CV for details
There you can find my ML research projects in UCU ML Lab
Unfortunately, I cannot share details on projects in production experience except the general description
Contact me on Telegram or here
Work experience:
- Middle Data Scientist (NLP) in UA/USA product startup (Shelf, Round B $52.5 million)
- SWE Internship in Google Switzerland (NLP related project)
- Machine Learning Researcher (CV) in UCU ML Lab
- R&D Engineer Internship in Huawei Amsterdam (Speech Recognition)

Total > 3 years

Education:
- Computer Science BSc in Ukrainian Catholic University

Key responsibilities and roles:
- I have powerful Math background due to study in Lviv Physics Mathematics Lyceum and UCU, participating in highest rank international mathematical competitions (IMO, IMC). Same applies to Algorithms and Data Structures
- I have broad experience with Data Analysis, Machine Learning (supervised/unsupervised), Computer Vision, Natural Language Processing, Scientific Computing, Statistical Inference concepts
- I'm able to do domain research from scratch, figure out relevant papers and tools, quickly analyze and reproduce SoTA approaches
- I have a solid vision of how the research lifecycle should look like and I prefer coworkers to follow these rules (Domain exploration, Exploratory Data Analysis, Hypothesis generation, Controlled experiments, Experiment Tracking (artifacts, code, data), Demo / Presentation)
- I have an experience working in product companies, both startups (Shelf) and FAANG (Google) so I have fluency in building ML and Data pipelines, CI/CD on cloud infrastructure (AWS)
- I can solely design solutions for business challenges, communicate with customers, security engineers, infrastructure engineers, lead small R&D teams, setup fluent research guidelines
- My work philosophy includes writing structured, readable, documented code (even in research); conducing and tracking controlled experiments; presenting results in the way understandable by non-tech people (Demo, Presentation)
- No MLOps / Data Engineer
- Lviv / Remote
- Preferable not purely engineering positions (research / engineering tradeoff)
- I expect the company / team to have solid work culture (responsibility, motivation, open-mind)
- I expect the company to follow horizontal hierarchy between managers and delevopers
- Part-time employment, etc is possible; I'm ready to discuss details
Position: Machine Learning engineer, Data analytics, Python Developer
Category: Data Science
Experience: 1 years | English: fluent

Last month by analyzing company data (which I work with) I realized that we can improve our sales by suggesting correct courses to people based on their age, interest and past knowledge, courses that they have participated and etc so I made a clear graph and showed it to my manager and they find it very interesting. After putting this data into work our sales improved by 7%.
Another accomplishment is I automated some of the work of my colleagues
I have been working as a Programming and robotics teacher for 3 years now. During these years I thought Python, web-dev, robotics, and other IT lessons. And at the same time, I was preparing freelance projects. For a year and a half now I have great interest in ML, Data analytics I have taken so many courses and prepared many projects like Heart-disease-predictor, Price-predictor, Image classification models and etc. My goal is to contribute to the companies in this field and develop my skills even further in this area. I'm especially interested in neural networks and the deep learning sphere by using Tensorflow
My greatest expectation and desire is to contribute to this field as much as possible and grow with the company that I'm going to work with.
Position: Machine Learning Engineer / Data Engineer
Category: Data Science
Experience: 6 years | English: fluent

Designed a serverless ML cloud architecture from scratch (AWS Cloud Formation/Terraform)
FleetyFly:
- Deployment and automatization of ML pipelines that optimize fleet efficiency and increase
clients revenue
- Design serverless ML cloud architecture from scratch (AWS Cloud Formation/Terraform)
- Prepared CI/CD pipeline for ML projects (AWS CodeBuild)
- Strategic decision about company data architecture
Wind Mobility GmbH, Department: Data.
- Created e-scooters rebalancing model that increases the revenue for the rebalanced e-scooters
by 50%
- Creating serverless ML architecture in AWS cloud (AWS Cloud Formation)
- Creation and deployment of ML models to production environment (Python, SparkSQL, AWS
Fargate)
- Data Engineering: moving data from operational to analytical DBs in serverless fashion (AWS
Glue, AWS Lambda, DataBricks), extensive unit/integration testing
- Optimization of complex logistics systems that increases numbers of rides in a city by up to
25%
Vueling Airlines, S.A., Department: Data.
- Created a model that predicts price and pax of every flight in the 3 months period with daily
accuracy below 10 % MAPE
- Developed and implemented end to end data science pipelines from scratch for supervised
and unsupervised ML models (PySpark/Python)
- Was part of Data Engineering team that deploys and automates ML models to production
env. Provided logging and monitoring pipelines (Python, MLFlow)
Lidl Stiftung & Co. KG, Department: Advanced Analytics.
- Developed ML algorithms (regression and classification) (sk-learn)
- Performed exploratory data analysis and business analysis, data visualization (R, Python,
SQL)
- Improvement of sales model (association analysis etc.
Most important is to work on interesting projects, and hands on work on developing ML solution, deploying architecture for ML and data. I would prefer keep the meetings to minimum (of course some meetings are necessary, but some aren't :) ). Working with experienced people from which I can learn, or with whom I can brainstorm is beneficial of course, but I also have experience with working in a startup where I was the first DS/ML Eng.
Position: Machine Learning Engineer / Data Engineer / ML Ops
Category: Data Science
Experience: 2.5 years | English: fluent


Data Engineer at Amazon
• Distributed data quality framework design and development (personal project)
• Working with Cloud Development Kit for automated resources deployment
• Internal fleet load investigation and optimization

Data Engineer / ML Ops at Receptor.AI
• Parallelization of machine learning and biochemical computations
• Managing public cloud and an own cluster
• Building startup infrastructure from scratch

Big Data Analyst at Kyivstar
• Constructed datasets using Apache Spark, Hive and Oracle DWH
• Look-alike model development
• Created analytical reports using Power BI (B2B, B2G)

Junior Fullstack developer at Prophy science
• Developed REST API via Flask Python3 web framework
• Frontend development with React JS framework
Position: Machine Learning Engineer / Data Science
Category: Data Science
Experience: 7 years | English: fluent


Finishing PhD thesis in informatics at Goethe University Frankfurt am Main
Multiple talks at different conferences like VVS, ECVP (mostly vision sciences)
Summer schools on machine learning (multiple)
Have successful track of implementing different solutions based on the machine learning in industry and for startups.
Have experience on neural networks (Deep Learning, CNN, etc.) for computer vision, but also worked with standard classification algortihms, NLP problems etc.
Remote, part-time presence possible
Position: Machine learning engineer, data scientist
Category: Data Science
Experience: 2 years | English: fluent

- 1 journal article published in Applied Sciences (first author)
- 3 conference papers published at EMBC 2021 and APSIPA ASC 2020, 2021 (first author)
- Holder of UK EPSRC scholarship for postgraduate research 
- CodeJam to I/O for Women coding competition winner (February, 2019): As one of the top 150 competitors (out of 2000+ participants) won a ticket and reimbursement to offset travel expenses to Google I/O 2019, Mountain View, California, USA.
- 1st place at Hackathon 4.0: Data for Future by Microsoft (October, 2018): Created platform for analyzing the poverty rate in Kazakhstan within the 24 hours. Won 3000 USD for the team.
- 1st place at "Digitalization of the ecosystem of health and social protection. Blockchain” hackathon by Unicef (May, 2018): Created iOS mobile application with a synchronizable glucose measuring device for children suffering Type 1 Diabetes. Won 1000 USD for the team.
MPhil in Applied Maths (to be awarded), MSc in Electrical and Computer Engineering and just tech enthusiast experienced in academic research looking forward to start a career in industry. 

In 2021 got a full scholarship and stipend from Engineering Engineering and Physical Sciences Research Council for conducting research in numerical methods applied in chemistry as a PhD student at the University of Manchester. Developed mathematical approaches to infer kinetics as a
system of ODEs from catalytic reactions time-series data. Decided to transfer to MPhil, expected to graduate in February 2023. Technologies used: Python (numpy, sci-py, cvxpy), Matlab. 

Worked as a teaching assistant at the Department of Mathematics, University of Manchester. Taught tutorial sessions for Calculus (1st year undergrads),  Programming with Python (2nd year undergrads), Statistical Inference (3rd year undergrads). 

In 2019-2022 developed machine learning and signal processing methods for brain-computer interfaces as a research assistant in Applied Signal Processing laboratory. As a result published a journal article in Applied Sciences and 3 conference papers, including IEEE EMBC 2021 conference. Technologies used: Python (numpy, pandas, sklearn, pytorch), Matlab.  

Education: MPhil in Applied Maths, MSc. in Electrical and Computer Engineering, BEng. in Automation and Control.

Languages: English (IETLS: 7.5),  Russian (first language), Kazakh (second language)
Position: Machine learning engineer / Data scientist
Category: Data Science
Experience: 5 years | English: intermediate


5 years with ML and DL. 
Expertise in computer vision, audio analysis, digital signal processing. Some experience with NLP.

Education:
* MS in Applied Math.
* MS in Computer Science.
* >10 passed ML/DL related courses
Long-term projects in a strong team with at least one more ML/DS engineer. Product companies, flexible hours are plus.
Position: Machine learning engineer / Data scientist
Category: Data Science
Experience: 5 years | English: fluent

o  Erasmus Mundus Scholarship, ‎€ 44.000, 2014 – 2016 (7 grants, 824 applicants)
o  Bronze Medal in International Physics Olympiad (Mexico, 79 countries, 395 participants )
o  excellent academic profile
- Python (NumPy, SciPy, Scikit-learn, OpenCV, PyQt, Pandas,  basics of TensorFlow)
- MatLab/Octave, Wolfram Mathematica
- basics of R / SQL / C++ / C#
- machine learning, computer vision, parallel computing
- open source project management, testing and distribution (see my GitHub profile bellow)
- have relevant certificates at EDX / Coursera / Udacity (13 in total, see the transcript

I have a strong background in Computational Physics, Math and modeling. I did a joint degree MSc in applied and engineering physics studying in the top institutions of  3 different countries (Germany, France, Switzerland).
I firmly believe that big data powered artificial intelligence (AI) will make the revolution in many different fields of our life in the nearest future. I would like to work in passionate teem on the challenging and innovative projects building AI/ML based solutions and/or applying the ML technics for data analysis/research. Transfer to another city and remote work is possible.
Position: Machine learning engineer / Data scientist
Category: Data Science
Experience: 1 years | English: intermediate

PhD in Physics and Mathematics (scientific background in Statistical Modeling and Image Processing); Kaggle ML competitions (top 10%, top 25%).
Skills: • Machine Learning - Classification, Regression, Clustering (Python, Scikit-learn, R); • Data cleaning, feature selection, normalization and preprocessing (Pandas, Numpy, Scipy, Matlab); • Data Analysis and Visualization (Pandas, Matplotlib, R); • Natural Language Processing (NLTK); • Time Series Analysis; • Image Processing (pyOpenCV, Matlab); • Cloud Computing (Amazon AWS); • Big Data (pySpark); • Artificial Neural Networks, Deep Learning (Keras – Theano, Tensorflow); • Programming Languages: Python, Matlab, R.
Looking for a job in the field of Data Science, Machine Learning.
Position: Machine learning engineer/Data scientist
Category: Data Science
Experience: 5 years | English: upper

My models generated more than $250000 potential revenue increase per year.
Data scientists with software engineering background. 
5+ years in IT total, 4 years in Data Science/Machine learning.
Domains: computer vision, fraud detection, forecasting.
Position: Machine learning engineer / Data Scientist
Category: Data Science
Experience: 2 years | English: intermediate


September 2018 – present
BI Developer
Description: 
BI solutions development
     Creating BI solutions using Python and Power BI. Anomaly detection in the data, business process optimization, cost reduction, KPI forecasting and creation of convenient tools for making management decisions. 
Responsibilities: 
• Data transformation and preprocessing
• Data modelling and analysis
• Implementing complex business logic using Python and Power
BI
• Using ML algorithms for business process automatization
• Data visualization
Position: Machine Learning engineer/Data Scientist
Category: Other
Experience: 2 years | English: fluent


Machine Learning Engineer with practical experience of building Machine Learning pipeline. As a bonus I'm Software Engineer with more of 13 years of experience in building and maintaining software solutions for different industries. Able to implement and maintain application from back-end with database to front-end. Solid understanding of Java/J2EE stack.

Finished with distinction 6 courses of Machine Learning specialization. Completed 3 Capstone projects: User Churn analysis, User Identification in the internet, Sentiment Analysis. 

Fields of study covered: Math, Statistics, Supervised Learning, Unsupervised Learning, Clustering, Trees, Gradient Boosting, Time Series methods, NLP. Python and libraries: Pandas, Numpy, SciPy.
At the moment I’m looking for Machine Learning/ Data Science tasks where I could apply my coding skills and Machine Learning knowledge. I see no issues to start using any new language or approach at work, but I get maximum working performance staying focused at one thing at a time. Highly appreciated if this thing would be Machine Learning.
Position: Machine Learning Engineer / Data Scientist
Category: Data Science
Experience: 5 years | English: fluent


Computer Vision Engineer - ADA University
Core Computer Vision Researcher - Sign Language Recognition and Processing for Azerbaijani Language to make life more accessible for thousands 
• Developing LSTM and CNN models to achieve Continuous Sign Language Recognition: primarily focused on Sequential Models and 3D CNNs 
• Developing NLP algorithm for context aware grammatical sentence completion in Azerbaijani language 
• Video processing for Deep Learning models 
• Constructing efficient Data Management systems: daily stream of about 20gb of video data 
• Tech Stack: Python, TensorFlow, Keras, Convolutional Neural Networks (CNN), Long short-term memory Recurrent Neural Networks (LSTM RNN), ANN, OpenCV, PostgreSQL, Git / Github, Linux

Principal Blockchain Data Scientist - DigData Startup
Core Data Scientist tasked with leading a team to analyze and uncover the trends in Decentralized Finance and Exchanges, and cryptocurrency • Developing a variety of metrics and dashboards to aid DeFI investors in decision making • Working on Clustering Algorithms to perform behavior analyses on anonymous on-chain data • Tech Stack: Python, TensorFlow, Keras, DNN, Git / Github

Machine Learning Tutor - Galactech School

Contributing to future job market by: 
 Teaching future innovators industrial level applications of classical Machine Learning and Deep Learning: have instructed 80 students by now 
 Designing and supervising a series of educational products on Data and AI in the EdTech ecosystem: 2 products currently being marketed at high value 
 Tech Stack: Python, Data Analytics, Regression Analysis, Classification, Clustering, Deep Neural Networks, Excel, SQL, PowerBI

Computer Vision Engineer - Crowthink

Intelligent Solutions startup where I: 
 Coded a production level Facial Recognition system deployed on AWS to perform as an additional application-level security protocol: model accuracy above 98% 
 Implemented a Recommendation Systems 
 Tech Stack: Python, Pytorch, CNN, OpenCV, Django