Davide Valeriani

Key Skills

Software

CVAT

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Segmentation

Classification

Computer Programming Coding

Data Collection

Freelancer Overview

With over 6 years of experience leading data science teams and developing AI-driven products, I specialize in designing robust machine learning frameworks and managing the full data lifecycle—from data labeling and annotation to model deployment. My background includes building and optimizing data pipelines for large-scale health and wearable sensor datasets at organizations like Technogym, Google, and WHOOP, where I have overseen the collection, labeling, and quality assurance of training data for computer vision, time series, and biomedical signal processing applications. I am highly skilled in Python, SQL, MLOps, and prompt engineering, and have a strong track record of collaborating with cross-functional teams to ensure data quality and drive impactful, production-ready AI solutions. My work spans computer vision, neurotechnology, and digital health, with a proven focus on rigorous data management, experimentation, and technical excellence.

Labeling Experience

Medical Imaging & Wearable Sensor Data Annotation for Machine Learning Models

CVATImageSegmentationClassification

Led and contributed to large scale data labeling and annotation projects supporting machine learning and AI research in healthcare and wearable technology. Responsibilities included annotating and validating medical images (MRI and histological images) through segmentation and classification tasks, labeling multimodal physiological time-series data (EEG, EMG, and wearable sensor signals), and performing quality assurance to ensure high annotation accuracy and consistency. Collaborated closely with researchers, clinicians, and engineers to define labeling guidelines, resolve edge cases, and maintain high data integrity standards. The annotated datasets supported the development and validation of predictive models for neurological disorders, sleep analysis, cardiovascular health, and human machine interaction systems, adhering to strict research, ethical, and data quality protocols.

Led and contributed to large scale data labeling and annotation projects supporting machine learning and AI research in healthcare and wearable technology. Responsibilities included annotating and validating medical images (MRI and histological images) through segmentation and classification tasks, labeling multimodal physiological time-series data (EEG, EMG, and wearable sensor signals), and performing quality assurance to ensure high annotation accuracy and consistency. Collaborated closely with researchers, clinicians, and engineers to define labeling guidelines, resolve edge cases, and maintain high data integrity standards. The annotated datasets supported the development and validation of predictive models for neurological disorders, sleep analysis, cardiovascular health, and human machine interaction systems, adhering to strict research, ethical, and data quality protocols.

2018 - 2024

Education

Work History