Position: Machine Learning Engineer
Category: Data Science
English level: fluent
Years of experience: 4

- Developed and successfully implemented sports modeling using Markov Chains that enabled the whole project's growth
- Built and deployed successful models that predict sport and e-sport events' outcomes with proven effectiveness and business value
- Build NN architectures that solved project problems such as Predict Symmetry, Custom Distributions for output layer, Hero Pick Embedding for League of Legends, and DOTA 2.
- Wrote a Neural Network backend using Golang to deploy models trained on Keras.
Experienced in ML project life cycle: 
data collection, preprocessing, feature engineering, training, data augmentation, validation and testing, post-deployment monitoring.

Knowledge of Deep Learning solutions: Convolutions, LSTMs, Transformers, Word Embeddings, Residual Connections, Transfer Learning, Model Pretraining and Fine Tunning, Graph Networks.
Good knowledge of natural language processing (Regexp, text preprocessing, indexing, vectorization tf-idf, Embeding representaions, Word2Vec, Glove, BERT).
Good knowledge of image processing and augmentation methods (Resizing, Hue shifting, Cropping, Denoising etc.).
Good knowledge of inner workings of SOTA architectures in Computer vision and NLP (GAN, VAE, BERT, GPT3, ResNET, U-Net, Syren).
Can create new Neural Network architectures to solve project’s problems.
Experienced in solving tasks with limited data.
Experienced in classic ML algorithms:
	Linear Models, Tree-based models, SVM, Boosting, KNN, Markov Chains, Bayesian Optimization

High dimensional data visualization: PCA, TSNE, UMAP
Clustering algorithms
Statistical hypothesis analysis
Experience working with 3D ML (SDF, Point clouds)
Experienced in implementing of Genetic algorithms.
Basic knowledge of Reinforcement Learning: Q-learning, Deep Q Networks
I am looking for a company to grow with and achieve goals with. Preferably with its own product. Not interested in banking and/or financial projects.