Position: ML/DL: Head, Lead, Chief, Architect, Director
Category: Data Science
Experience: 11 years | English: upper

Project management:
- Computer Vision (Human Pose Estimation, Object Detection, design PoC & MVP); (hyper) parameter searching  &  optimization  (Grid  &  Random  &  Manual); supported  DNN  learning  (CNN);  formed  the  big sets (train,  val,  test);  executed  ETL; 
implemented preprocessing stages  (improved image quality, cropping); realized model structure modifications (built TF Lite model); completed interim results analysis & model interpretations;  decision making;  code review/refactoring;  team  –  3-5  (hiring,  scaling, performance management); code review. 
- NLP  (tasks:  NER,  text classification,  sentiment analysis,  tokenization, lemmatization);  model  (transformers)  implementation;  spaCy,  NLTK;  cleaning,  pre-processing,  and feature engineering;  code review;  team  –  3-5  (hiring,  scaling, performance management); code review.

I have two Ms. degrees in CS & in Financial and Economic Security Management; Ph.D. in CS; Dr.Sc. (Hab. degree) in  Economics (Math + Security). Have 60+ different certificates in DS/ML/DL. Author of several publications with IEEE (h-index = 12). 13 R&D projects in general (Banking, FinTech, Statistics); Project/Product Manager in 4 of them; the invention of new math methods & approaches, connected different scientific areas, created new models, simulation.
Experience professional stack: IT, Programming: 25+; Data Science: 25+; Security: 20+; Python: 7+; Management: 25+; ML/DL: 10+.
 
Throughout my career, I worked as a teammate, Team & Tech Lead, Chief Data Officer, and ML Solutions Architect. Several times I've gathered a team & department from scratch and mentored the participants. Over the years I've had data science projects which involved full-cycle ML/DL model development. I have successfully mentored and managed teams of data scientists & ML engineers (up to 25). Additionally, built and deployed different ensemble models using ML algorithms. My strength as an employee is not only in the model-building participation but in solving the business problem using an ML/DL model with applying full-cycle DS/ML project serving.

I worked in the following areas & with tasks: Computer Vision (Human Pose Estimation: Hourglass, HRNet, Custom, BlazePose, Open Pose, PoseNet, DensePose; Object Detection; Instance & Semantic Segmentation design PoC & MVP); FinTech; Banking (Churn prediction); Energy; Images & Video streaming; NLP (NER with transformers & Spacy, text classification, sentiment analysis, tokenization, lemmatization); image classification; image generation & augmentation, time series analyzing/forecasting (ARIMA, LSTM, FBProphet) classification, clustering, sentence classification; face detection & person identification; Anomaly/Fraud Detection; A/B testing. Have the experience to build projects from scratch: from PoC to MVP (deployment).

What I do at my last workplace:
- implementation of deep learning and classical machine learning approaches on tabular data;
- data wrangling, cleaning, imputting, transforming, scaling, ETL, EDA, anomaly detection, missing data handling, etc.;
- implemented statistical methods to identify trends and relationships between different features & targets;
- feature selection & engineering;
- trained models for insurance (automotive industry) claim  & profit prediction (regression task), claims binary / multiclass classification. time series forecasting;
- usage of the approaches: cluster analysis, pattern recognition, regularisation, dimension reduction, ensemble methods
- model's validation;
- (hyper)parameter’s  searching  &  optimization  (Grid, Random, Manual, Neptune, Autokeras, Optuna);
- planned and built new deployment architectures;
- trained and tuned neural networks (MLP);
- models explanations by different approaches (LIME, ELI5, build-in, SHAP, DALEX).