Position: Data Scientist, CV Engineer, NLP Engineer, ML Engineer
Category: Data Science
English level: upper
Years of experience: 2.5

I am Google Cloud Certified Professional Machine Learning Engineer. I have a PhD degree.
Graduated from the Faculty of Computer Technologies
and Power Engineering at State Biotechnological University. 
IT courses:
1. ML Engineering for Production (MLOps) on Coursera (by DeepLearning.AI, in progress)
2. Python for Computer Vision with OpenCV and Deep Learning
3. dlcourse.ai (by OpenDataScience)
4. cs231n (by Stanford University)
5. mlcourse.ai (by OpenDataScience)
6. Machine Learning (by Stanford University, certificate)
I am Google Cloud Certified Professional Machine Learning Engineer. I have an academic background (PhD degree) that provides a deep understanding of  the mathematical basis of classical Machine Learning and Deep Learning algorithms (Computer Vision, NLP). I also have extensive experience in processing various types of data and extracting conclusions based on the methods of mathematical statistics, probability theory, fuzzy set theory, optimization theory, etc. 

I have worked on a number of ML and DL projects, including:
CV:
App for conversation 2D images to 3D models
(PyTorch (Deep Learning part), OpenCV, Blender3D)
Video stream face detection system (OpenCV,
single-short detector)
Object detection and recognition system (OpenCV)
Face recognition system (OpenCV, FaceNet)
Deep Learning based system for binary
classification of images (PyTorch, transfer learning)
NLP:
Нierarchical text classification system (PyTorch,
contrastive learning, graph encoding)
System for text processing (PyTorch,
BidirectionalLSTM, word2vec)
Spam filter system (MATLAB SCL)
ML:
Speech generation system (PyTorch, voice
convertion)
Deep Learning based service for forecast of solar
radiation (PyTorch, Pandas, NumPy, Ray Tune,
Matplotlib, REST API)
Credit scoring system (ScikitLearn, Pandas, NumPy)
Anomaly detection system (MATLAB SCL)
Recommender system (MATLAB SCL)

My general Data Science skills: experience of scientific research and their application in practice, linear algebra, statistics and probability theory, fuzzy set theory, etc.

Methods and algorithms of classical ML: Pandas, NumPy, ScikitLearn, SciPy , etc.

Deep Learning stack - PyTorch, Tensorflow, Keras, Neural Network architectures and approaches (CNN, LSTM, others),  CV architectures and  algorithms, NLP algorithms (BERT, GPT-3) 

General engineering skills: GoogleCloud, Docker, PostgreSQL, Github, Linux, Couchbase
I would like to work in an open team where you can develop and gain experience from each other