Data Analyst • Statistical Modelling • Scientific & Biotech Background (PhD)
15+ years of experience in scientific data, statistical modelling, and data‑driven improvement across research, biotech, and industrial environments.
About Me
I am a Data Analyst with 15+ years of experience working with complex datasets in scientific and operational environments. My background includes a PhD in bioprocess engineering and extensive work in statistical modelling, R/Python analytics, QC pipelines, and reproducible workflows.
I specialize in structured investigations, trend analysis, predictive modelling, and data‑driven decision support. My scientific training gives me a strong foundation in experimental design, uncertainty, and rigorous analysis—skills I now apply across multiple domains.
Core Competencies
Data & Analytical
- R (tidyverse, ggplot2)
- Python (developing)
- Statistical & predictive modelling (regression, ANOVA, multivariate)
- Data cleaning, QC, reproducible workflows, dashboards, reporting
- Excel (Pivot Tables, basic Power Query)
- SQL
Technical & Scientific
- Experimental design
- Biological systems understanding
- Process optimisation
- Technical investigations
Collaboration & Leadership
- Cross‑functional teamwork in scientific, biotech, and data‑driven environments
- Training, documentation, and knowledge transfer across technical teams
- Structured, analytical problem‑solving in regulated settings
- Clear technical communication for both technical and non‑technical audiences
What I Work On
- Analysing scientific and operational datasets to identify trends and patterns
- Using data and modelling to understand variability and support decision‑making
- Applying structured investigations and root‑cause analysis to complex problems
- Designing reproducible analytical workflows that improve reliability and clarity
- Translating complex findings into clear, actionable insights for stakeholders
Selected Projects
Data & Analytics
- R‑based analysis pipelines for process monitoring and trend detection.
- Statistical modelling to understand variability and reduce deviations.
- Applied AI/ML use cases to support technical decision‑making.
Bioprocess & Operations
- Process stabilization and robustness improvements in large‑scale cultivation.
- Optimization of upstream parameters to improve yield and consistency.
- Equipment readiness and reliability initiatives supporting continuous production.
Contact
For opportunities in data analytics or hybrid technical roles in life science, contact me at ioannis.dogaris@gmail.com.