Marco Russo

Bioinformatics, MLOps & Applied AI Consultant

I work at the intersection of bioinformatics, machine learning, MLOps, and applied analytics. Over the last decade, I have helped companies, academic environments, and product teams turn complex data problems into usable systems, reproducible workflows, and better technical decisions.

My current work is especially focused on health data science, clinical and biological datasets, reproducible research pipelines, and production-grade ML infrastructure. Alongside delivery work, I also teach data mining and applied analytics, which keeps my approach practical, structured, and communication-oriented.

Specialist profile

Bioinformatics, health data, and research workflows

  • Genomic and multiomics analysis support
  • Clinical and observational data preparation
  • Reproducible pipelines in Python and R
  • Decision-support analytics and interpretable ML for health applications

Machine learning, MLOps, and data engineering

  • End-to-end ML workflows from problem framing to deployment
  • Cloud-based orchestration using AWS, GCP, Azure, Kubernetes, and Airflow
  • Production data pipelines, QA, monitoring, and reproducibility
  • Applied modeling for research, product, and operational use cases

Analytics and measurement background

  • Digital analytics strategy and measurement design
  • GTM and GA4 implementation, auditing, and governance
  • Business intelligence, data visualization, and reporting systems
  • Translation of noisy operational data into actionable decisions

Teaching and training

Teaching has been a constant across my career. Before specializing in biomedical AI and bioinformatics, I spent years designing and delivering training in analytics, business intelligence, machine learning, data visualization, and digital measurement.

That work began in 2012 and expanded through business schools, postgraduate programs, online delivery, and in-company training. Across classroom, remote, webinar, and academic formats, I have trained more than 30,000 professionals and students.

Areas taught over the years include:

  • Data science and machine learning foundations
  • Python, R, data mining, and applied modeling
  • Business intelligence, ETL, SQL, and dashboarding
  • Digital analytics, Google Tag Manager, and Google Analytics
  • Data visualization with Power BI, Tableau, and related tools

Institutions and collaborators have included Aula Creactiva, Camara de Comercio de Madrid, EAE Business School, IEBS, IEDGE, KPI’s Digital School, Neoland, Data School, and university collaborations including the UOC.

Background and current focus

Before moving deeper into life sciences and bioinformatics, my professional base was built in business intelligence, big data, digital analytics, and data engineering. I worked across retail, finance, insurance, industry, and digital environments where delivery had to be measurable, maintainable, and aligned with business constraints.

That background still shapes how I approach current bioinformatics and health-data work: with a strong emphasis on automation, reproducibility, monitoring, documentation, and realistic deployment conditions.

Today, my focus includes genomic analysis, health-data science, cloud-native ML systems, and computational workflows that can move from exploration to production without losing rigor.

Beyond work

Outside technical work, I enjoy time with my family, cooking, chess, and playing electric guitar.