Position: Data Scientist / Machine Learning Engineer
Category: Data Science
English level: fluent
Years of experience: 8


Previously worked as an iOS/macOS Developer (for 6+ years).
4+ years ago my passion switched to DS, AI and ML area.

Higher education — The University of Bristol (United Kingdom).
Strong background in Econometrics & Statistics.
English — Fluent, IELTS score 8.0 / 9.0.

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• Computer Vision
• Time Series forecasting (+ stationarization)
• Recommender Systems (Content-based / User-based / Hybrid)
• Multimodal & Multitask deep learning
• Supervised / Unsupervised / Transfer learning
• Creating custom layers, callbacks & loss functions
• Model Uncertainty & Monte Carlo simulation
• Ensemble modeling

• keras / tensorflow / fastai
• pyspark / jupyter notebook
• numpy / pandas
• scikit-learn / scipy / matplotlib
• random tree / random forest
• bootstrapping / bagging
• boosting (xgboost) / ensemble
• opencv (cv2) / PIL

• MLP / CNN / RNN / ResNet / Encoder-Decoder
• Agglomerative Hierarchical Clustering (AHC)
• Similarity measures / Cosine distance / Euclidean distance
• Hyperparameters optimization / Bayesian optimization
• Overfit / Underfit prevention (+ regularization)
• Dropout (+ MC Dropout as Bayesian Approximation)
• Data mining / Data augmentation
• Data normalization / Data standardization

• Warm Restarts
• Convolution / MaxPooling
• GlobalAveragePooling
• BatchNormalization / LayerNormalization
• TCN (Dilated Causal Convolution)
• LSTM (+ Bidirectional LSTM)
• Embedding / Reshape / Dense / Flatten
• ReduceLROnPlateau / EarlyStopping