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I am a dedicated Data Engineer with a strong foundation in data analytics, specializing in building production-grade data infrastructure, distributed database systems, scalable ETL pipelines, and AI-powered data applications. With 7 years of experience, I help companies: • Transform messy data into automated, reliable, and scalable ETL pipelines for complex financial data that save 100+ hours monthly • Architect distributed database systems: Design multi-tier data architectures for massive-scale record management • Build advanced ETL pipelines with intelligent change detection to process only what's changed, reducing unnecessary processing • Architect cloud-native data solutions and scale data infrastructure across multiple clouds • Automate data workflows to eliminate manual processing and escape Excel Hell • Build Information Retrieval (IR), semantic search, OpenSearch, and Retrieval-Augmented Generation (RAG) systems that make massive datasets, documents, and knowledge bases searchable and actionable • Develop AI-powered data applications, including document intelligence, chatbot, and agent-based solutions integrated with existing business workflows • Modernize legacy data systems with cloud & AI solutions • Optimize data infrastructure to reduce costs and improve performance • Design parallel execution frameworks: Implement isolation patterns enabling concurrent pipeline runs without conflicts • Build data foundations for AI initiatives, including vector search, knowledge retrieval, document processing, and AI automation workflows I have been providing a wide range of services in the realm of data analytics and data engineering such as: • ETL/ELT pipeline orchestration: Prefect, Databricks Workflows & Asset Bundles • Lakehouse & distributed database architecture: Databricks/Delta, YugabyteDB (distributed PostgreSQL), Neo4j, OpenSearch • Custom dbt materializations and incremental models: SCD-2, temporal tables • Database optimization and storage compression • Data validation, quality quarantine & reconciliation frameworks • Per-run schema isolation and parallel pipeline execution • Google Sheets automation and dashboard generation • Excel-to-database migration and formula translation • Multi-source data integration: CSV, Parquet, S3, APIs, databases • Data Cleaning & Transformation at scale • Operational Efficiency Analysis • Financial metrics calculation systems • Multi-cloud architecture design • Infrastructure as Code • Cloud services integration: AWS, GCP, Azure • RAG & LLM data applications: grounded citations, document extraction, NL-to-SQL semantic layers • Automated reporting solutions (Python-Excel integration) • Data Visualization & Dashboarding While the above services encapsulate my core offerings, I am inherently adaptable and thrive on diving into new challenges and expanding my skill set. Seeking great, enthusiastic projects that will provide me with challenging, interesting work that I can learn from and contribute to. My stack: Data Engineering: ✅ Python ✅ SQL (PostgreSQL, MySQL, SQL Server, YugabyteDB, DuckDB) ✅ Prefect ✅ dbt ✅ PySpark ✅ Delta Lake ✅ Databricks Cloud & Infrastructure: ✅ AWS (EC2, S3, Glue, RDS, Lambda, EKS, DynamoDB, ECR, Bedrock) ✅ Google Cloud Platform (BigQuery, GKE, Bigtable, Cloud Functions) ✅ Azure (ADF, Synapse, AKS, Cosmos DB, Azure Functions, ACR, Text Analytics) ✅ Terraform ✅ Docker ✅ Kubernetes ✅ Prometheus Data Storages: ✅ RDBMS (PostgreSQL, MySQL, SQL Server, DuckDB) ✅ Object Storage (S3, Wasabi) ✅ Graph Database (Neo4j) ✅ Key-Value Database (Redis, DynamoDB) ✅ Document Database + Search Engine (OpenSearch) ETL & Data Processing: ✅ Pandas ✅ NumPy ✅ Selenium ✅ BeautifulSoup Spreadsheet Automation: ✅ Google Sheets API (gspread) ✅ Excel automation (openpyxl, xlwings) ✅ Automated dashboard generation Data Visualization: ✅ Matplotlib ✅ Seaborn ✅ Plotly ✅ Power BI ✅ Grafana Backend Development: ✅ FastAPI ✅ Flask ✅ RESTful APIs ✅ GraphQL ✅ Redis ✅ Nginx ✅ Gunicorn ✅ WebSocket AI/LLM & Agent Systems: ✅ OpenAI API ✅ Anthropic API ✅ Gemini API ✅ AWS Bedrock ✅ Agent Development ✅ AI Chatbots ✅ RAG ✅ NL-to-SQL semantic layers ✅ Semantic search

I build practical Machine Learning models and Data Science solutions from data preprocessing to deployment in web applications. Built models trained on 5k–10k images, achieving ~85–90% accuracy on test datasets. Implemented end-to-end model deployment and integration, including feature preprocessing, API setup, and front-end visualization. My focus is on Computer Vision and Deep Learning projects, delivering solutions that work reliably in real-world environments. Skills: Tools/Frameworks: Python, TensorFlow (Keras), TensorFlow.js, Scikit-learn, OpenCV, Jupyter, ONNX ML & DL: Computer Vision, YOLO, Object detection, CNN, RNN, LSTM, Transformers Deployment & Integration: AWS, Azure, API integration, browser-based models Data Science & Analytics: Data Preprocessing, Data Collection, Feature Engineering, Exploratory Data Analysis (EDA), Data Visualization

Data analyst and Python-based data scientist with an MSc in Astrophysics and Cosmology. I work with complex datasets, applying statistical analysis, machine learning, and data visualization to produce reliable, actionable insights. Experienced in Python (Pandas, NumPy, scikit-learn), SQL, and scientific data workflows. Research-oriented, detail-focused, and results-driven.

Hi there! I'm Shynggys — a data scientist, analyst, and research consultant with 8+ years of experience turning raw data into business-ready insights. I help teams and organizations clean, explore, and model their data using a structured, result-oriented approach — combining technical tools with real-world logic. ⚙️ My skills include: • Python (pandas, scikit-learn, matplotlib, statsmodels) • Machine Learning (classification, regression, forecasting, NLP) • Data Cleaning, Feature Engineering, EDA • Dashboards in Power BI, Tableau, Excel • SQL, database design, ETL pipelines • Web scraping & API integration (BeautifulSoup, Selenium) • Survey & qualitative research analysis (SPSS, MaxQDA) • Structuring and writing data-driven research with strong methodology 📊 Typical projects I work on: ✔ Credit risk and churn prediction models ✔ Market behavior analysis & reporting ✔ Education and policy research based on real data ✔ Automating data workflows for small teams GitHub portfolio → github.com/ShynggysTorez 🤝 I've worked with: • Startups scaling through smarter analytics • Academic teams writing advanced research reports • Government and consulting clients using data for better strategies My goal is always the same: to turn complex data into clear, explainable results that help people make decisions. Let's connect if you're looking for someone who brings both data depth and communication clarity to the table.

Data Scientist / Statistician I am a Data Scientist /Statistics PhD Student seeking opportunities in the technology industries. My interest is to solve problems from a statistician's perspective, and to build tools

More than 10 years of experience in startups, specialising in machine learning and R&D. Chief Scientific Officer of IPnote startup (USA), scientific director of the Master's programme "Machine Learning Engineering". Winner of AstanaHub Battle 2023 and participant of TechCrunch 2023.