Result-driven data professional with over 9 years of experience both as a data scientist and AI product owner. I have Built multiple AI solutions across diverse infrastructures (cloud and on-premises).
My strong technica…
Result-driven data professional with over 9 years of experience both as a data scientist and AI product owner. I have Built multiple AI solutions across diverse infrastructures (cloud and on-premises).
My strong technical skills coupled with a deep understanding of the business allow me to build impactful solutions.
Trained the isolation forest algorithm on time series sensors data to detect 85% of equipment anomalies.
Created a cloud-based web application.
Established automated daily data pipelines.
Created an automated machine learning pipeline to predict turbine production loss using only meteorological time series data, saving the business 250k€ in the first month only.
Built a recommendation engine for projects durations based on similarities.
Developed a web application serving 200+ end users.
Processed 1.5 TB of numeric and text maintenance and operations data.
Calculated the failure rate for each valve type.
Demonstrated their reliability.
Saved 400k€ of testing.
Developed pipelines with Python and Airflow to define equipment maintenance strategy.
Avoided human errors.
Saved the business 1.5 month of work yearly.
Use case 1 - Created a Predictive Maintenance Solution for solar inverters (Business: EDF Renewables USA)
Trained the isolation forest algorithm on Time Series sensors data to detect 85% of equipment anomalies.
Created a cloud-based web application, and established automated daily data pipelines.
Use case 2 - Created a machine learning model to predict wind farms Production loss (Business: EDF Renewables USA)
Created an automated machine learning pipeline to predict turbine production loss using only meteorological time series data, saving the business 250k€ in the first month only.
Use case 3 - Created a recommendation engine for Project Planning duration (Business: Nuclear Engineering)
Built a recommendation engine for projects durations based on similarities.
Developed a web application serving 200+ end users.
Use case 4 - Demonstrated Valves reliability (Business: Hinkley Point Engineering)
Processed 1.5 TB of numeric and text maintenance and operations data, and calculated the failure rate for each valve type, demonstrating their reliability and saving 400k€ of testing.
Use case 5 - Automated Equipment Maintenance Strategy Definition (Nuclear Engineering)
Developed pipelines with Python and Airflow to define equipment maintenance strategy, avoiding human errors and saving the business 1.5 month of work yearly.
Implemented anomaly detection methods to prevent data-centers network breakdowns.
Defined data science solutions and KPIs for the nuclear outage management department.
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