ML Data Associate
I am a detail-oriented data scientist and ML associate with hands-on experience in data labeling, annotation, and AI training data workflows, particularly at Amazon. My work spans labeling and annotating structured and unstructured data—including images, text, audio, and video—for machine learning and generative AI models. I have performed tasks such as bounding box annotation, segmentation, classification, and transcription, ensuring high accuracy and strict adherence to quality guidelines. My technical toolkit includes Python, SQL, AWS SageMaker Ground Truth, Appen, and key ML libraries like Pandas, NumPy, and Scikit-learn. I have contributed to over 10 critical workflows, collaborating closely with data scientists and engineers to optimize dataset quality and model performance. My project experience covers NLP, sentiment analysis, and fraud detection, demonstrating my ability to handle diverse data domains and deliver actionable, high-quality training data under tight deadlines.