Samantha Heiskell

Key Skills

Software

Appen

Clickworker

CloudFactory

CVAT

Data Annotation Tech

Labelbox

Label Studio

OneForma

Remotasks

Scale AI

Snorkel AI

Toloka

Telus

Mindrift

Roboflow

Top Subject Matter

Artificial Intelligence & Machine Learning

Financial Services & Business Analytics

STEM Fields: Math, Physics, and Chemistry

Top Data Types

Image

Text

Video

Top Task Types

Bounding Box

Classification

Cuboid

Emotion Recognition

Prompt Response Writing SFT

Freelancer Overview

I have extensive experience in data labeling and AI training across multiple domains, including natural language processing (NLP), computer vision, and scientific fields such as math, physics, and economics. My background in business and STEM disciplines enables me to annotate and label complex datasets with precision, ensuring that AI models are trained on high-quality, accurate data. I have worked on diverse projects, labeling financial data for market predictions, annotating scientific datasets for research purposes, and enhancing AI models in various industries, including healthcare and robotics. My familiarity with AI tools such as TensorFlow and PyTorch, combined with my strong analytical skills, allows me to effectively contribute to AI training tasks that require detailed attention to data quality and integrity. In addition to my technical skills, I am proficient in managing multiple data labeling projects simultaneously, working collaboratively with cross-functional teams to meet project deadlines. My commitment to quality assurance ensures that the data used for AI training is both accurate and aligned with industry standards. Whether contributing to large-scale AI projects or smaller, specialized tasks, I bring a detail-oriented approach and a strong problem-solving mindset to every project.

Labeling Experience

Text Generation

MindriftTextEntity Ner ClassificationQuestion Answering

In this project, I worked on annotating and labeling financial datasets to train a large language model (LLM) for predicting market trends and stock movements. The project involved curating a diverse set of financial documents, including historical stock data, news articles, and financial reports. I applied text classification, named entity recognition (NER), and sentiment analysis techniques to label key financial terms, company names, market trends, and sentiment shifts. The labeled data was used to train an LLM capable of analyzing and predicting stock price fluctuations and market behavior, enhancing decision-making in investment strategies and risk management. My contributions ensured that the model could interpret financial data with high accuracy and relevance to real-time market changes.

In this project, I worked on annotating and labeling financial datasets to train a large language model (LLM) for predicting market trends and stock movements. The project involved curating a diverse set of financial documents, including historical stock data, news articles, and financial reports. I applied text classification, named entity recognition (NER), and sentiment analysis techniques to label key financial terms, company names, market trends, and sentiment shifts. The labeled data was used to train an LLM capable of analyzing and predicting stock price fluctuations and market behavior, enhancing decision-making in investment strategies and risk management. My contributions ensured that the model could interpret financial data with high accuracy and relevance to real-time market changes.

2024 - 2024

LLM machine learning

OneformaTextEntity Ner ClassificationQuestion Answering

I annotated text data to train a Large Language Model (LLM) for NLP tasks. Using OneForma, I applied NER and classification to label entities like names, locations, and dates, while also contributing to text generation and fine-tuning. This enhanced the model's ability to generate coherent responses and perform complex language tasks for applications like chatbots and text analysis.

I annotated text data to train a Large Language Model (LLM) for NLP tasks. Using OneForma, I applied NER and classification to label entities like names, locations, and dates, while also contributing to text generation and fine-tuning. This enhanced the model's ability to generate coherent responses and perform complex language tasks for applications like chatbots and text analysis.

2023 - 2024

Data Annotation and Labelling

CVATImageBounding BoxPolygon

In this project, I provided data annotation and labeling for a large image dataset used to train an AI model for object detection in autonomous vehicles. Using CVAT, I applied bounding boxes, polygons, and segmentation labels to accurately identify and classify objects such as vehicles, pedestrians, and road signs across various environmental conditions. My work ensured consistency and accuracy, contributing to a high-quality dataset that improved the model's ability to detect and react to real-world objects, enhancing the vehicle’s safety and navigation capabilities.

In this project, I provided data annotation and labeling for a large image dataset used to train an AI model for object detection in autonomous vehicles. Using CVAT, I applied bounding boxes, polygons, and segmentation labels to accurately identify and classify objects such as vehicles, pedestrians, and road signs across various environmental conditions. My work ensured consistency and accuracy, contributing to a high-quality dataset that improved the model's ability to detect and react to real-world objects, enhancing the vehicle’s safety and navigation capabilities.

2023 - 2024

Image annotation

Scale AIImageBounding BoxPolygon

I annotated a diverse dataset of images for training AI models used in self-driving cars. Using Remotasks, I applied bounding boxes, polygons, and segmentation techniques to label key objects such as pedestrians, vehicles, road signs, and obstacles. This labeled data helped improve the vehicle’s ability to detect and navigate through complex environments, enhancing the vehicle’s AI system for safer autonomous driving. My work ensured the model could accurately recognize and respond to real-world objects in various driving conditions.

I annotated a diverse dataset of images for training AI models used in self-driving cars. Using Remotasks, I applied bounding boxes, polygons, and segmentation techniques to label key objects such as pedestrians, vehicles, road signs, and obstacles. This labeled data helped improve the vehicle’s ability to detect and navigate through complex environments, enhancing the vehicle’s AI system for safer autonomous driving. My work ensured the model could accurately recognize and respond to real-world objects in various driving conditions.

2022 - 2024

LLM machine Learning

Scale AITextEntity Ner ClassificationQuestion Answering

I annotated financial datasets, including earnings reports and market news, to train an LLM for stock trend prediction and market sentiment analysis. Using techniques like text classification, sentiment analysis, and NER, I labeled key data such as company names and financial indicators. This enabled the model to generate insights for investment strategies and market predictions, improving decision-making in the financial sector.

I annotated financial datasets, including earnings reports and market news, to train an LLM for stock trend prediction and market sentiment analysis. Using techniques like text classification, sentiment analysis, and NER, I labeled key data such as company names and financial indicators. This enabled the model to generate insights for investment strategies and market predictions, improving decision-making in the financial sector.

2022 - 2024

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Education

Work History