MSc Chemistry → Data Engineer
R&D, Biotech, Pharmaceutical, and Scientific Data domain expertise combined with modern data engineering, validation, analytics, and AI-assisted workflow development.
I am a data-focused technical professional with a strong scientific background in Chemistry (MSc Chemistry), additional business and industrial exposure through BSc Oil & Gas Management, and teaching experience that helps me design clear, user-friendly solutions.
My portfolio is built around the intersection of:
- scientific domain expertise
- data engineering workflows
- validation and quality systems
- analytics and visualization
- AI-assisted reporting and summarization
-
🧪 Chemistry and life-science domain expertise
Experience aligned with R&D, laboratory, and pharmaceutical-style data workflows -
👨🏫 Teaching and communication background
Ability to translate technical complexity into accessible, structured solutions -
🤖 AI-assisted workflow integration
Practical use of LLM-based summarization, automation, and analytical tooling -
📊 Production-minded engineering approach
Focus on readability, documentation, validation logic, and reusable code structure
- Languages: Python 3.10+
- Data: pandas, numpy, SQL, SQLite
- Validation / Testing: pytest, Pydantic
- Scientific / Chemistry: RDKit
- Dashboards / Visualization: Streamlit, Plotly, py3Dmol, HTML/CSS/JavaScript
- AI / Automation: LLM integration, document summarization workflows
- Tools: Git, VS Code
This repository contains a set of portfolio projects that demonstrate a progression from:
- structured data ingestion
- ETL and transformation
- quality validation
- AI-assisted summarization
- chemistry-specific analytics
- relational storage
- interactive dashboard development
Each project is organized as a standalone portfolio piece with its own code, setup instructions, and documentation.
A chemistry-aware ETL pipeline for pharmaceutical or laboratory-style records. This project focuses on transforming raw structured inputs into cleaner outputs with domain-aware checks and validation logic.
Highlights
- pH validation with chemistry-aware rules
- temperature and concentration checks
- unit conversion support
- structured quality reporting
- production-style error handling
Technologies: Python, pandas, sqlite3, ETL patterns
An AI-assisted research summarization workflow for technical and scientific documents. This project demonstrates document handling, summarization design, and user-friendly explanation output.
Highlights
- LLM-compatible summarization workflow
- PDF text extraction
- teaching-mode summaries
- batch-style processing support
- demo-friendly operation
Technologies: Python, LLM integration, document processing, analytical summarization
A pharmaceutical-style data quality and profiling project that evaluates dataset consistency, completeness, and anomaly patterns with domain-aware checks.
Highlights
- statistical profiling
- quality scoring
- anomaly detection
- automated reporting
- chemistry-aware validation logic
Technologies: Python, pandas, numpy, statistical analysis
A Python-based chemical data validation and molecular visualization project that ingests structured compound records, validates chemistry-specific fields, generates JSON outputs, and produces an analytical dashboard with 2D and 3D molecular views.
Highlights
- SMILES and molecular data validation
- JSON report generation
- 2D structure visualization
- 3D molecular viewer
- bond-length and bond-angle metrics dashboard
Technologies: Python, RDKit, Pydantic, Pytest, HTML/CSS/JavaScript
Visual reference: Project 4 dashboard layout and viewer screenshots Source Source
A relational storage and export layer for validated chemistry records. This project demonstrates how structured scientific data can be persisted, synchronized, queried, and exported in reusable analytical formats.
Highlights
- relational schema for compound records
- JSON seed-data ingestion
- duplicate-safe synchronization
- SQLAlchemy + SQLite workflow
- CSV and Markdown export
Technologies: Python, SQLAlchemy, SQLite, Pandas, Pytest
A Streamlit-based molecular analytics dashboard that acts as the front-end control center for validated chemistry records. It combines 2D/3D visualization, reaction-step interpretation, energy-profile analysis, and conceptual reaction playback into a portfolio-ready analytical interface.
Highlights
- chemistry record browsing and filtering
- 2D molecular structure display
- 3D molecular viewer
- reaction pathway and energy intelligence
- conceptual 2D reaction playback with frame controls
- dashboard-style analytical presentation
Technologies: Python, Streamlit, RDKit, Plotly, py3Dmol
Visual reference: Project 6 dashboard and reaction playback screenshots
Projects 4, 5, and 6 form a connected chemistry analytics workflow inside this repository:
Validates structured chemical records, checks molecular data quality, and generates 2D/3D visualization-ready outputs.
Stores validated chemistry records in a relational structure, supports synchronization and export, and acts as a persistence layer for downstream analytics.
Provides the user-facing interactive dashboard with molecular views, reaction pathway insights, energy charts, and conceptual playback controls.
Together, these projects demonstrate a complete progression from validation → storage → interactive analytical presentation. Visual examples from the dashboard and playback workflow are shown in the latest Project 6 screenshots Source Source Source
| Metric | Value |
|---|---|
| Projects Featured | 6 |
| Domain Focus | Chemistry / Pharma / Scientific Data |
| Workflow Coverage | ETL, Validation, AI Summarization, Relational Storage, Dashboards |
| Core Strength | Domain-aware data engineering with scientific context |
| Code Style | Structured, documented, validation-oriented |
- Real chemistry and pharmaceutical-style validation logic
- Scientific context applied to technical engineering problems
- Use cases aligned with R&D, biotech, and laboratory data
- ETL workflows
- AI-assisted summarization
- data quality analysis
- relational storage
- dashboard development
- molecular visualization
- clear documentation
- teaching-influenced explanation style
- accessible analytical outputs
- portfolio-friendly presentation
This is not only a collection of isolated scripts — the repository shows how data can move through:
- ingestion
- validation
- storage
- reporting
- visualization
- interactive user-facing analysis
Foundation: ETL basics + domain validation
Advanced: AI-assisted summarization + document handling
Quality analytics: profiling + anomaly detection
Scientific validation + 2D/3D molecular visualization
Relational persistence + export workflows
Interactive dashboard + molecular analytics + reaction playback
# Clone the repository
git clone https://github.com/JesusAs2019/technical-analytical-project.git
cd technical-analytical-project
# Choose a project
cd 04-chemdata-validation-engine
# or
cd 05-chemdata-relational-vault
# or
cd 06-chemdata-control-center
# Install dependencies
pip install -r requirements.txt
# Run the project
# (see each project README for exact commands)
# Run demo
python src/pipeline.py # (adjust filename per project)
Project-Specific Setup
Each project includes:
✅ Detailed README with usage instructions
✅ Sample data for testing
✅ requirements.txt with dependencies
✅ Working demos
🎓 Background
Education
MSc Chemistry
BSc Oil & Gas Management
Professional Strengths
scientific and analytical thinking
structured problem solving
user-friendly explanation design
domain-aware engineering mindset
(R&D, Renewable Energy, Pharmaceutical focus)
Career Direction -Transition Journey:
Chemistry research → Data analysis
Teaching → User-centric design → Data engineering
Why Data Engineering:
Combine chemistry expertise with technical skills
Build solutions for scientific data challenges
Bridge gap between domain experts and technology
Bringing together scientific domain knowledge and modern engineering practices to build useful, well-documented, analytics-oriented software solutions.
📫 Contact
GitHub: JesusAs2019
Open to:
Data Engineer roles
R&D /Pharma / Biotech / Life Science analytics opportunities
Technical consulting and project-based work
Scientific data workflow and validation projects
🏅 Professional Positioning
Target profile: Data Engineer / Technical Analytics / Scientific Data Workflow specialist
Competitive Advantage
Chemistry MSc + data engineering portfolio
domain expertise in validation and scientific records
strong crossover between technical implementation and subject-matter context
capable of building both backend workflows and user-facing analytical dashboards
📝 License
MIT License — free to use and modify.
🙏 Acknowledgments
Built with technologies and tools including:
Python
pandas
numpy
SQL / SQLite
RDKit
SQLAlchemy
Streamlit
Plotly
py3Dmol
pytest
LLM-assisted workflow patterns
Last Updated: July 2026