Position: Data Scientist\ML Engineer
Category: Data Science
English level: fluent
Years of experience: 4

Competitions
•	Quora Insincere Questions Classification (top 4% on Kaggle)
	Given a question, predict whether the question is sincere or insincere(toxic, based on an exaggerated tone, rhetorical, etc.). Used a combination of pre-trained embeddings, LSTM and Attention with a cyclic learning rate to score top 4% on the private leaderboard.
•	Santander Customer Transaction Prediction (top 9% on Kaggle)
	On an anonymized, normalized dataset extract insights and identify whether a customer will make a specific transaction in the future.
•	Vodafone Age Prediction Challenge (top 8)
•	Data Science Game 2018 (Kaggle)
	Time series forecasting on a large database of user information and user transactions to predict whether on a given date a person will be interested in buying a certain stock or not.
•	Google Hashcode 2018, 2019
	Path optimization for self-driving cars  throughout graphs with a timetable.(2018)
Machine Learning Consultant at Prophecy Labs, Brussels
• Lead the implementation of a ML system of several NLP models, LLMs and recommendation systems.
• Best practices in MLOps (containerization, CI/CD, clean code principles, effective use of git, deployment, monitoring, experiment tracking etc)
• MLOps infrastructure setup
• Developed an NLP recommendation system to match
assignments with experts

Machine Learning Engineer at Giphy
Projects: Working on implementing and improving ML solutions, finding new data signals and utilizing them to gain an advantage using Machine Learning. Optimizing current services, researching new content manipulation and generation ML solutions, optimizing runtime and deployment of new models.The key components of the work include:
• Image to image translation (Style Transfer, Neural domain shift)
• Text-guided image transformation (Generative modeling, VQGAN, CLIP, Transformers, StyleGAN)
• Strobe detection (Improving adaptability to new content and
   detection)
• Model runtime optimization and deployment (ONNX, torchscript, Seldon, TensorRT)
• Search Engine(Textual and Imaging embeddings, ANN algorithms, Elastic Search)
Machine Learning: PyTorch, Tensorflow, Keras, ONNX, Numpy, Pandas, Scikit-learn, HuggingFace
Infrastructure: AWS, Google Cloud, Kubernetes, Seldon, Elastic Search Engineering: Python, Flask, Docker, Jenkins, TensorRT, PostgreSQL,
Gunicorn, GRPC, REST,NMSLib, OpenCV.


AI Researcher at Vocalics AI
End-to-end research, development, and deployment of Machine Learning models and algorithms to gain a competitive advantage in the field of Speech Synthesis (TTS). Highly independent research from ground up, implementing and improving newly released papers and ideas, finding ways to incorporate them into the main product.
• Speech synthesis systems(Tacotron2, Mellotron, Transformers, generative models based on GANs and VAEs, Representation Learning, Recurrent NeuralNetworks, Speaker characteristics extraction)
• Vocoding models (VocGAN, WaveNet, HiFi-GAN)
• Speech enhancement models(UNet GAN,Signal
Decomposition)
• Speaker extraction and voice transfer(i/d/x-vectors, neural
speaker extraction)
Machine Learning : PyTorch, Tensorflow, Scikit-Learn, HuggingFace, FastText, TorchAudio, Librosa,
Engineering: Python, Docker, MySQL, Flask, Jupyter, Pandas, Scipy, PyTest, Java

Education:
[BSc] Applied Mathematics, National University of Kyiv-Mohyla Academy
[MSc] Applied Mathematics, National University of Kyiv-Mohyla Academy
Enthusiastic about extracting and presenting insights from raw data, as well as building intelligent, data-driven solutions. Eager to advance my studies in Machine Learning by applying these practices to solve real-world challenges.