Description

I am a motivated data scientist with many years of experience applying machine learning, deep learning, and data mining techniques to extract actionable insights. My work spans object and anomaly detection, predictive modeling, time series analysis, and image processing. I have a strong background in handling sensor data, integrating signal processing and deep learning to address challenges in data analysis and data fusion. With a solid foundation in statistics, mathematics, and big data analytics, I turn complex datasets into meaningful, decision-support insights.

My current activities:

Data collection, cleaning, denosing,

Data mining and knowledge extraction,

Data fusion,

Data Analytics & Reporting.

Time Series Analysis/Forecasting,

I use the following technologies in my daily tasks:

Python,

Machine/deep learning Models

PowerBI,

AWS

Languages

English
Native or bilingual
German
Basic

Workplace preferences

Can work on-site

Bremen (up to 50km)

Constructor group,
Data Scientist
September 2024 - Today (1 year and 9 months)
Bremen, Germany
• Develop and enhance AI models for the positioning, localization and object detection.
• Work on data analysis, data cleaning, training and testing reliable Supervised deep learning models to estimate depth in 2D images.
• Work on marketing analysis based on Bayesian mixed-media model (MMM) to analysis effect of spend channels and revenue.
Deep Learning TensorFlow Business intelligence Machine learning
INESC TEC,
Data Scientist
November 2020 - August 2024 (3 years and 9 months)
Porto, Portugal
• Analyzed geospatial data from various sources, including maps, satellite imagery, and benchmark datasets.
• Develop a novel and highly accurate framework for predictive maintenance in compressors and integrate with explainable AI techniques to ensure transparency and comprehensibility of models' decisions.
• Development and implementation of end-to-end frameworks using deep learning techniques for anomaly/fault detection, failure, remaining useful life (RUL) prediction, and anomaly explanation.
Deep Learning Machine learning Python Pytorch time series analysis
SYSTEC, University of Porto,
Grant Holder
April 2018 - November 2020 (2 years and 7 months)
Portugal
• Developed mapping and localization algorithms based on state es timation, probabilistic filtering, and integrated navigation for au tonomous underwater vehicles.
• Designed a graphical user interface on Matlab for the purpose of data integration from multiple real sensors (IMU, camera, GNSS, DVL, LiDAR).
• Implemented a Visual and Inertial Odometry framework for state estimation of autonomous cars.
Data science Python Machine learning TensorFlow