Mikel Añibarro Ortega

R&D Data Scientist, PhD

Bridging the gap between scientific complexity and actionable business insights. Focused on machine learning, advanced data analysis, and AI engineering.

About Projects Skills Experience LinkedIn GitHub

About Me

I'm a PhD Chemist turned Data Scientist, combining 6+ years of rigorous research and experimental design with hands-on expertise in data analysis and machine learning. Currently deepening my knowledge in AI Engineering and MLOps, with a focus on building systems that don't just model reality, they ship to production.

My expertise spans tabular ML, multivariate analysis (PCA, PLS, LDA), experimental design (DoE, RSM), and A/B testing. I bridge the gap between complex technical data and clear business strategy—whether in FoodTech, Pharma, or Industrial optimization.

What I bring: Scientific precision meets scalable solutions. I don't just analyze data; I prescribe next moves.

🎯 Scientific Rigor

Trained in hypothesis testing, experimental design, and statistical validation. Every insight is reproducible and defensible.

🔄 Cross-functional Bridge

5+ years mentoring international teams. I translate between technical complexity and business strategy effortlessly.

📊 Tabular Data Expert

Specialized in ML on structured data. I know when to use simple statistics vs. complex models—and why.

🌍 Domain Deep Dives

Food science, chemistry, pharma background. I understand the domain, not just the algorithms.

Featured Projects

1

Bioactive Extraction Optimizer

Predictive modeling for phytochemical recovery

Problem: Standardizing Aloesin extraction from Aloe vera by-products required manual, inefficient experimental iterations.

💡 Solution

Deployed a Response Surface Methodology (RSM) model that maps solvent/temperature/time relationships to yield optimization. Transformed experimental data into an interactive decision-making tool that reduces time-to-optimal-extraction by ~70% and minimizes solvent consumption.

🔬 Technical Core

  • Analytical: HPLC-DAD-ESI/MSn quantification of Aloesin
  • Design: Central Composite Rotatable Design (CCRD) with Reduced Cubic Models
  • Impact: 70% reduction in optimization time and solvent consumption, reproducible predictions

🛠 Tech Stack

Python Streamlit Statsmodels Plotly Scikit-learn
🚀 Launch App 💻 GitHub
2

Food Labeling Audit

Uncovering hidden truths in food industry data

Problem: Food labeling is contradictory and often misleading. Current standards (NutriScore, BIO seals) have blind spots that enable manipulation.

💡 Key Findings

  • The NutriScore Hack: Blind to industrial processing; ultra-processed get A/B ratings by adding isolated fiber/protein
  • BIO Label Limitation: Guarantees sustainability but not nutritional quality—ecologic ≠ nutritious
  • Private Labels Win: Against stereotypes, store brands offer cleaner labels, less sugar, more fiber than big corporations
  • Plant-based Paradox: In meat-culture countries, vegan alternatives prioritize taste mimicry over protein density, adding chemical complexity

📊 Dataset & Scope

  • 10,000+ products from OpenFoodFacts snapshot
  • 20 nutritional & labeling features
  • Cross-segment analysis: categories, brands, regions

🛠 Tech Stack

Python Pandas Matplotlib Seaborn
💻 GitHub (Notebook + Viz)
3

NutriScore 2.0

Transparent, manipulation-proof food classification

Problem: Original NutriScore is incomplete & manipulable. It doesn't account for additives, processing complexity, or "false healthy" products.

💡 Solution

Built a custom additive classification system using EFSA safety data + PubMed API mining, then applied K-means clustering on enriched feature space. Result: A food classification that detects manipulated products and "false healthy" items that pass original NutriScore.

🔬 Methodology

  • Data Engineering: EFSA additive database + PubMed API for bioavailability/risk data
  • Feature Creation: Additive toxicity index, processing complexity score, nutrient density
  • Modeling: K-means clustering validated with Hierarchical Clustering dendrogram convergence
  • Robustness: Detects "false healthy" products (NutriScore A/B but flagged in 2.0)

📊 Dataset

  • Complete OpenFoodFacts database (~800k products)
  • Engineered features: additives, nutritional profiles, processing markers
  • External validation: EFSA + PubMed data integration

🛠 Tech Stack

Python Scikit-learn K-means Streamlit APIs
🚀 Launch App 💻 GitHub

Skills & Expertise

📊 Data Science & ML

  • Machine Learning (Supervised & Unsupervised)
  • Tabular Data Analysis
  • K-means, Hierarchical Clustering
  • Classification & Regression
  • Feature Engineering & Selection
  • Model Validation & Evaluation

📈 Statistical Analysis

  • Multivariate Analysis (PCA, PLS, LDA)
  • Design of Experiments (DoE)
  • Response Surface Methodology (RSM)
  • A/B Testing & Hypothesis Testing
  • ANOVA & Statistical Inference
  • Central Composite Designs

💻 Programming & Tools

  • Python (Pandas, NumPy, Scikit-learn)
  • SQL & Database Queries
  • Streamlit & Plotly (Visualization)
  • Jupyter Notebooks & Git
  • APIs & Web Scraping

🌐 Soft Skills

  • Cross-cultural Team Leadership
  • Mentoring & Knowledge Transfer
  • Technical Communication (Tech & Non-tech)
  • Stakeholder Management
  • Project Coordination
  • Problem-solving & Adaptability

🔬 Domain Expertise

  • Food Science & Technology
  • Analytical Chemistry
  • Bioactive Compounds
  • Functional Foods
  • By-product Valorization
  • Pharmaceutical/Nutraceutical Development

🗣️ Languages

  • Spanish (Native)
  • Basque (Native)
  • English (C1 - Professional)
  • Portuguese (C1 - Professional)

Professional Experience

Research Scientist

Jan 2025 — Oct 2025

Polytechnic Institute of Bragança (IPB) | Portugal

  • Coordinated R&D data acquisition and analysis for cross-functional research teams
  • Mentored 5+ junior researchers; ensured data quality, methodological rigor, and independence
  • Translated complex quantitative findings into actionable research insights

PhD Candidate & Researcher

Sep 2019 — Apr 2025

University of Vigo / IPB | Spain & Portugal

  • Multivariate Analysis: Identified correlations between extraction methods, chemical composition, and biological activities using PCA, PLS, DoE, and RSM
  • Experimental Design: Central Composite Rotatable Design (CCRD) optimizations; A/B testing of nutraceutical formulations
  • Publications: Author of 30+ ISI-indexed scientific papers; 50+ conference communications
  • Impact: PhD awarded Cum Laude with International Distinction; co-inventor of 1 patent (PT117256-A)

QC Laboratory Technician

Nov 2015 — Jun 2016

FYM - Italcementi Group | Spain

  • Executed standardized analytical tests on cement samples using XRF and chromatographic techniques
  • Investigated data outliers; identified root causes and implemented corrective actions

Laboratory Attendant

Feb 2017 — Jul 2017

Terai Cosmética | Spain

  • Executed cosmetic formulation processes under GMP protocols

Education

PhD in Agri-Food Science & Technology

University of Vigo & Polytechnic Institute of Bragança | 2019–2025

🎓 Cum Laude with International Distinction

MSc in Chemistry & Pharmacy of Natural Products

University of Salamanca & Polytechnic Institute of Bragança | 2017–2019

BSc in Chemistry

University of the Basque Country (EHU) | 2010–2016

Professional Certifications

IBM Data Science Professional Certificate (2025) | Google Advanced Data Analytics Specialization (2025) | The Bridge Data Science & AI Bootcamp (2026)

Key Achievements

30+ ISI-indexed publications | 50+ international conference presentations | 1 Patent (PT117256-A) | Young Editorial Board Member in Foods and Antioxidants journals.