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Viktoria T.
🇺🇦Ukraine (UTC+02:00)
Created AtUpstaffer since July, 2024

Viktoria T. — Data Science Engineer

Expertise in Data Science, AI and Machine Learning.

Last verified on July, 2025

Core Skills

Python
Python
Computer Vision (CV)
Pandas
Pandas
ML
ML
AI

Bio Summary

Data Science engineer with over 3 years of practical commercial experience in Natural Language Processing (NLP), Computer Vision (CV), and Recommender Systems. Available skills in data analysis using machine learning approaches to satisfy business needs, problem-solving, and other tasks in this sphere. A person, focused on obtaining the best results, using all knowledge and skills. Friendly and ready to help the team complete tasks and solve certain problems.

Technical Skills

Programming LanguagesPython
AI & Machine LearningAI, Deep Learning, Hugging Face, Keras, Kubeflow, Mlflow, NLP, NumPy, OpenCV, PyTorch, Scikit-learn, Spacy, TensorFlow, TensorFlow Serving, YOLO
Python Libraries and ToolsKeras, Matplotlib, NLTK, NumPy, Pandas, Plotly, poetry, PyTorch, Scikit-learn, SciPy, Streamlit, TensorFlow
Data Analysis and Visualization TechnologiesDVC, ML, Pandas
Databases & Management Systems / ORMMySQL
Amazon Web ServicesAWS RT
Google Cloud PlatformGCP Storage, Google BigQuery
Deployment, CI/CD & AdministrationCI/CD, Jenkins
Virtualization, Containers and OrchestrationDocker
Version ControlGit
SDK / API and IntegrationsPayment Gateways
Scripting and Command Line InterfacesRegexp
Third Party Tools / IDEs / SDK / ServicesSublime Text
Other Technical Skillsargparse, Custom API, Deep Learning (DL), Kubeflow for ML pipelines, Label Studio, MMCV, ONNX, Recommender Systems, Voxel51

Work Experience

Data Scientist (CV), Person Detection and Face Recognition

Duration: Duration: 1 year 1 months

Summary: The main goal was to classify people focusing on their actions and to recognize specific persons using their faces. 

Responsibilities: 

  • Creating and engineering training data for a model.
  • Implemented dataset preparation and model fine-tuning code for model evaluation.
  • Evaluating different approaches to data preparation.
  • Developing pipeline steps for the training model.
  • Implementing new pre-trained models

Technologies: Python, Pandas, Pytorch, YOLO, RT-DETR

Data Scientist (NLP), PDF Text and Table Extraction

Summary: Extracting textual information from non-editable PDFs for quick collection and analysis. The task was related to recognizing and classifying text and tables in the picture using the "AWS Textract" service.

Technologies: Python, Matplotlib, Plotly, NumPy, Pandas, OpenCV, Regex, Spacy, AWS Textract

Duration: 4 months

Data Scientist, Online Retail Product Recommendations

Summary: Developed product recommendations system for online retail with analysis of historical user data. Developed a pipeline to generate popular items based on time, price, etc.

Responsibilities: 

  • Developed a recommendation system using implicit data
  • Evaluated model offline and online (A/B tests) scores
  • Developed model serving app and evaluated its performance

Technologies: Python, GCP, Tensorflow, Kubeflow

Duration: 6 months

Data Scientist (CV), Infrastructure Log Analysis

Summary: System for automated analysis of infrastructure logs which are row text data to discover the associated groups of resources that generate the logs based on extracted tags, ids, names, and other recognized entities.

Responsibilities: 

  • Using SQL-like databases for data extraction
  • Extracting data from SQL-like databases based on specific task queries.
  • Filtered and cleaned data to ensure accuracy and relevance.
  • Merged multiple datasets into a single dataset to optimize extraction time and resources.
  • Creating and filtering data using different patterns.
  • Applying K-modes model for clustering words
  • Preparing results taking into account the requirements of the
  • customer

Technologies: Python, Matplotlib, Plotly, NumPy, Pandas, OpenCV, Regex, Spacy, AWS Textract

Duration: 8 months

Data Scientist (CV), Game Reward Management System

Summary: System for managing custom winnings during a game using user information such as user level, VIP rank, user wallet, etc. and generated probabilities for reward items.

Responsibilities: 

  • Implemented economy manager for receiving reward items.
  • Created a simulation of the game using mock user data.
  • Created pytests for verifying created algorithms.

Technologies: Python, Pandas, NumPy, SciPy, dataclasses

Duration: 4 months

Education

  • Bachelor's degree in System analysis
  • 2019 - 2023

Certification

  • Data Science Camp Offline ML course at SmartInsight
  • 2021
  • Introduction to Data Science in Python
  • Coursera
  • 2020
  • Applied Machine Learning in Python
  • Coursera
  • 2020
  • Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
  • Coursera
  • 2022
  • Convolutional Neural Networks in TensorFlow
  • Coursera
  • 2022
  • Data Science Methodology
  • Coursera
  • 2022
  • Google Cloud Big Data and Machine Learning Fundamentals
  • Coursera
  • 2023

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