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Viacheslav Zhenylenko
Verified Expert in Engineering
Machine Learning Developer
Viacheslav has eight years of experience in data science and software engineering, focusing on Python, with production experience in Java and C++. He is passionate about the insights gained from raw data and enjoys converting them to create exceptional business value. Viacheslav is well-versed in applying advanced ML techniques, such as computer vision, NLP, product recommendation systems, networking data, and classical data science to solve data-heavy projects.
Portfolio
Experience
Availability
Preferred Environment
Eclipse, Visual Studio Code (VS Code), PyCharm, Jupyter, MacOS, Linux, Vim Text Editor, Sublime Text, Bash
The most amazing...
...project I've developed was an industry-first, self-reconfigurable AutoML system for congestion detection in RAN networks.
Work Experience
Data Scientist
Creating Possibilities Limited
- Developed a parallel data collection process for hundreds of websites.
- Utilized LLMs for summarization, topic extraction, sentiment analysis, report generation, and other tasks.
- Deployed tool on AWS with Batch, EventBridge, Lambda, and others.
Data Scientist
Classic J. Vans INC.
- Developed tools to automatically gather information from multiple web sources in a structured way.
- Utilized LLMs for Named Entity Extraction and classification from the raw text.
- Utilized Selenium with a proxy to emulate web clients.
- Deployed tool on AWS using EventBridge, ECS, Lambda, S3, QuickSight, SES, and others.
Data Scientist | Engineer
Spectation Sports LLC
- Developed models for MMA pre-fight prediction and in-fight forecasts.
- Utilized multiple machine learning techniques, such as boosted trees, regression, ensembling, stacking, imputation, and others.
- Performed multistep feature engineering for individual fighters.
- Identified, statistically, the most valuable features and best external sources of this data.
- Deployed and integrated models into existing data pipelines, including elements of MLOps methodologies.
AI Engineer
Briefly
- Utilized OpenAI models for multiple tasks for productivity and customer success domains. Performed fine-tuning, RAG, agents, function calling, and others.
- Developed distributed cloud infrastructure using Terraform, Amazon Elastic Container Service (Amazon ECS), Amazon DynamoDB, Amazon EventBridge, Amazon Simple Queue Service (SQS), AWS Lambda, and Amazon Simple Email Service (SES).
- Performed API integrations with external services and tools, including OpenAI, Google services, Slack, Notion, and Hubspot.
- Designed and built a number of Django APIs with business logic.
Senior Data Engineer
Grata Inc.
- Developed end-to-end distributed NLP-based geocoding pipeline using Celery on Kubernetes.
- Implemented scraping from company websites and aggregators.
- Developed and deployed on the AWS SageMaker web page category classification model.
- Implemented a hybrid geocoding model with query lookup, query relaxation, result validation, prioritization, and fallback mechanisms.
- Used combinations of available geo databases and offline entity extractors like libpostal and third-party geocoding services to combine it in a single view.
- Increased a fraction of parsed addresses, reduced incorrect addresses by 95%, and improved the overall data quality score by 20%.
Lead Data Scientist
Botprise, Inc.
- Developed the back end for the full ML cycle, ModelOps, and MLOps, on the platform. Added a wrapper on top of the AWS SageMaker.
- Worked on dozens of automation workflows (use cases), including MLOps, analytics, DataOps, networks, and ITOps.
- Created the back- and front-end elements using React for a drag-and-drop, chatbot-building application.
- Implemented and deployed dozens of algorithms (classification, clustering, time series, NLP, and computer vision) for different use cases.
- Led a small ML team, including planning, management, monitoring, and leadership.
Data Analyst
Spin (Tier Mobility)
- Performed a time-series forecasting of the demand for e-scooters for a global e-scooter rental company (hundreds of cities) with models for auto selection and auto retraining.
- Performed ad-hoc data analysis and built Looker dashboards.
- Performed an intervention-effect analysis (for promotions and other events).
Senior MLOps Engineer
Pro Football Focus, LLC
- Introduced and implemented MLOps techniques, tools, and approaches.
- Rebuilt a dozen monolithic R pipelines into distributed, modular, and functional-styled Python pipelines.
- Developed an MLOps layer on top of Dagster, Seldon, Feast, and other tools.
- Fine-tuned existing model hyperparameters both for speed and performance.
Machine Learning Engineer
Plutoshift, Inc.
- Introduced MLOps tools to existing infrastructure with Seldon, Feast, Great Expectations, and others.
- Migrated existing hardcoded models to introduce the MLOps infrastructure.
- Developed back-end APIs using Django for ML-related services.
- Implemented classification and time series forecasting models for manufacturing sensors.
Senior AI Developer
Akcelita
- Developed an ingestion and processing pipeline on AWS for photos from surveillance cameras.
- Experimented with various non-DL and DL approaches and tested them. I also used and trained Siamese Neural Networks with an attention mechanism, achieving 95+% accuracy.
- Created and shared presentations with analytics to executives and developed and maintained a Wiki for the project.
Team Lead
The National Academy of Sciences of Ukraine
- Led and mentored a team of students. I defined objectives and controlled the process using the Agile methodology.
- Created a tool for crop classification and map creation.
- Collected data manually and via web scraping using Mapillary and oversaw data labeling.
- Implemented and tested DeblurGAN and several other classic deblurring methods.
- Oversaw field localization (YOLO) and crop classification by fine-tuning a ResNet model.
Senior Data Scientist
Openwave Mobility
- Created a multi-staged data pipeline from raw packet data (TCP/IP layer) to consumable inputs for machine learning models with multi-processing implementation in Python (CPython).
- Trained, tuned, evaluated, and compared multiple machine learning models in Python (scikit-learn, Keras, XGBoost, CatBoost) and C++ (mlpack).
- Oversaw the data analysis and communication with stakeholders. Created a reusable Python tool for generating rapid and externally configurable data analysis reports.
- Implemented custom feature generation algorithms based on expert knowledge based on aggregation, derivatives, TCP/IP conversation delays, products, and fractions.
- Implemented custom, multi-staged feature selection algorithms that were model-based.
- Deployed and monitored the project in production in the network. If the tool detects congestion, optimization policies are applied. Customers reported up to a 20% increase in the quality of delivery for video content.
Data Scientist
Octetis
- Developed, deployed, and evaluated a hybrid recommendation engine in Python for an online store.
- Oversaw customer behavior analysis, visualization, and stakeholder communication.
- Handled different scenarios of user engagement using a strategy pattern. Contextual recommendations were given based on popularity (general and category-based), item-to-item, and SVD. (Python, scikit-learn, SciPy).
- Integrated recommendation engine into a Django back end.
- Conducted multiple A/B tests with random sampling for evaluation of the system. Compared to the most popular items in the category baseline, we achieved up to a 150% boost in purchases per session and increased revenue.
- Created an image super-resolution module for an online cloud site constructor with Keras.
- Utilized middle-deep CNN, trained on several blur kernels, and deployed it as a service via REST.
- Conducted surveys showing an increase of about 5% in satisfaction for users of the platform.
Software Engineer Intern
- Trained and evaluated AdaBoost models for customer churn prediction using FBLearner Flow.
- Performed hyper-parameters tuning for optimization.
- Data-engineered with Hive and processed data using Python.
Research Intern
Samsung
- Developed algorithms for smart keyboard functionality (word prediction and spelling correction).
- Developed Naive Bayes for n-grams and K-nearest neighbors (KNN) for spelling corrections.
- Created tweaks for better algorithm performance using Laplace smoothing and a custom keyboard distance for KNN.
- Developed algorithms with C++. Integrated them with a Java to Android keyboard and published them to the App Store.
Software Engineer Intern
Engage Point
- Developed a Content Management Interoperability System in Jakarta EE. I used the Model-view-controller framework for the application.
- Developed Enterprise JavaBeans for the business logic of the application.
- Developed JavaServer Pages for the presentation level.
Experience
Web Scraping Mapillary
Skills
Languages
Python, SQL, Python 3, Bash, R, Snowflake, Fortran, Java, C++
Frameworks
Django, Flask, Web Frameworks, Selenium
Libraries/APIs
Scikit-learn, Keras, TensorFlow, PyTorch, Pandas, NumPy, SciPy, Beautiful Soup, Dask, Natural Language Toolkit (NLTK), Matplotlib, REST APIs, OpenCV, React Redux, PySpark, React
Tools
Jupyter, PyCharm, IPython Notebook, Amazon SageMaker, Geocoding, GIS, Vim Text Editor, Sublime Text, Plotly, Apache Airflow, Git, Celery, Jenkins, AWS CloudFormation, Grafana, Looker, Microsoft Power BI, AutoML, RabbitMQ, BigQuery, Microsoft Excel, Spreadsheets, Tableau, Notion, Android NDK, OpenAI Gym
Paradigms
Data Science, Agile Software Development, REST, Unit Testing, ETL, Parallel Programming, DevOps, Concurrent Programming, Anomaly Detection, Business Intelligence (BI), Automation, Management
Platforms
Docker, AWS Lambda, Jupyter Notebook, MacOS, Amazon Web Services (AWS), Linux, Apache Kafka, Kubernetes, Visual Studio Code (VS Code), Eclipse, Android, Java EE, Google Cloud Platform (GCP), Azure
Storage
MySQL, PostgreSQL, Data Pipelines, Data Validation, MongoDB, Elasticsearch, Datadog, Databases, Apache Hive, Cassandra, Google Cloud
Other
Predictive Analytics, Predictive Modeling, Machine Learning Automation, Computer Vision, Data Analytics, Deep Learning, Machine Learning, Mathematics, Applied Mathematics, Statistics, Algorithms, Data Analysis, Dagster, Machine Learning Operations (MLOps), Prefect, Computer Science, Web Scraping, Data Scraping, Software Development, Artificial Intelligence (AI), Programming, Computational Science, Time Series Analysis, Convolutional Neural Networks (CNN), Object Detection, Seldon, Feast, Data Visualization, Data Engineering, Analytics, Neural Networks, Software Engineering, Linear Regression, Statistical Modeling, GeoPandas, APIs, Generative Pre-trained Transformers (GPT), ChatGPT, OpenAI GPT-3 API, OpenAI GPT-4 API, OpenAI, Large Language Models (LLMs), Language Models, Recommendation Systems, Natural Language Processing (NLP), Science, Scientific Computing, DataOps, Physical Science, Networking, Physics, Applied Physics, Transformers, TCP/IP, mlpack, Explainable Artificial Intelligence (XAI), Cloud Infrastructure, Google BigQuery, API Integration, Dashboards, Quality Assurance (QA), GPT, Internet of Things (IoT), HubSpot, HubSpot CRM, LangChain, Generative Adversarial Networks (GANs), Networks, FBLearner Flow, Classification, Apache Cassandra, Sports, Software Architecture, Sentiment Analysis, DVC
Education
Master's Degree in Theoretical Physics (Quantum Field Theory)
Kyiv National University - Kyiv, Ukraine
Master's Degree in Computer Mathematics and Algebra
Kyiv National University - Kyiv, Ukraine
Bachelor's Degree in Computer Science and Applied Statistics
Kyiv National University - Kyiv, Ukraine
Certifications
AWS Machine Learning Specialty
Amazon Web Services
AWS Solutions Architect Associate
Amazon Web Services
AWS Certified Developer Associate
Amazon Web Services
Data Science: Data to Insights
MITProfessionalX DSx | edX
Artificial Intelligence (AI)
ColumbiaX CSMM.101x | edX
Second Place
ACM-ICPC, Country level
Bronze Medal
International Mathematical Olympiad (IMO)
Second Place
Kyiv International Physics Festival
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