Development of biological products leveraging data-driven real-world evidence.

• Built automated analytics and reporting pipelines for production use.
• Integrated multi-source data using FAIR principles.
• Managed external data exchanges under regulatory and quality constraints.

Connected technologies (IoT) based on IMU sensors for a SaMD.

Director of Research, Data and Analytics (2022–2024)

• Led applied research and AI strategy from innovation to production.
• Guided ML and statistical models into validated, production-ready solutions.
• Ensured scientific rigor and regulatory compliance (FDA, CE, ISO 13485, GDPR).

Head of Data & Analytics (2020–2022)

• Defined the Data & Analytics strategy aligned with business and R&D goals.
• Led Analytics and BI teams, delivering KPIs and decision-support products.
• Turned clinical and business needs into actionable insights at scale.

Data Team Leader – Data Engineering & Foundations (2018–2020)

• Built and led the core Data team (Data Engineering, Data Science, BI).
• Designed cloud-based data platforms integrating devices and operational data.
• Established data governance, quality, security, and GDPR compliance.

Designed and validated devices using respiratory signal and data analytics (IQOS).

• Contributed to ensuring traceability, interoperability, reproducibility and compliance via INTERVALS data governance platform.
• Designed and evaluated metrics for machine learning models.
• Collaborated with R&D and IT teams on standardized data workflows and metadata harmonization.

Conducted data exploration and predictive modeling for chronic diseases in genetic epidemiology.

• Performed statistical programming and analysis on biological data.
• Designed and managed data pipelines, including cleaning, transformation, and QC.
• Managed a large-scale epidemiology data platform (5,000 families).

• External B2B analytics dashboards (client-facing Power BI, outcome tracking, usage KPIs, value demonstration).
• Internal productivity dashboards (team performance, data pipeline monitoring, study progress & delivery KPIs).
• Sales & business intelligence (revenue tracking, funnel analysis, customer segmentation, product adoption metrics).

Supporting transparent, reproducible, evidence-based R&D through advanced visualization and statistical exploration.

• Advanced analytical visualization (interactive dashboards, multi-study exploration).
• Statistical analyses (longitudinal studies).
• Scientific traceability and reproducibility (data, protocols, metadata).

Transforming life sciences and healthcare through modeling, simulation, and digital collaboration.

• Multi-scale visualization (model-driven views).
• Simulation-driven analytics (hypothesis testing, predictive outputs).
• Data interoperability (curated academic datasets feeding industrial modeling platforms).