Allan Waliaulah

Key Skills

Software

AWS SageMaker

Anno-Mage

Appen

Argilla

Deep Systems

Encord

Google Cloud Vertex AI

LabelImg

Labelbox

Mercor

Lionbridge

Other

Remotasks

Top Subject Matter

Financial datasets

quantitative finance

ML research

Top Data Types

Audio

Computer Code Programming

Document

Text

Image

Top Task Types

Classification

Computer Programming Coding

Bounding Box

Object Detection

Polygon

Segmentation

Entity Ner Classification

Point Key Point

Polyline

Text Generation

Text Summarization

Fine Tuning

Red Teaming

Question Answering

Evaluation Rating

Transcription

Data Collection

Function Calling

Prompt Response Writing SFT

RLHF

Cuboid

Freelancer Overview

Group Leader, MSc Financial Engineering (Data Annotation/Labeling). Brings 1+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Other and Remotasks. Education includes Master of Science, WorldQuant University (2026). AI-training focus includes data types such as Text and Image and labeling workflows including Classification and Bounding Box.

Labeling Experience

Group Leader, MSc Financial Engineering (Data Annotation/Labeling)

OtherTextClassification

Labeled and annotated financial datasets for supervised machine learning tasks to ensure consistency across training sets. Utilized Python, pandas, NumPy, and scikit-learn to preprocess data, define label schemas, and perform QA checks. Coordinated labeling workflows in a research environment for time-series forecasting and asset pricing models. • Defined and maintained label schemas for financial data annotation. • Conducted QA validation of annotations to maintain high data integrity. • Used Python-based tools to automate aspects of the labeling process. • Collaborated closely with student teams on dataset preparation for model training.

Labeled and annotated financial datasets for supervised machine learning tasks to ensure consistency across training sets. Utilized Python, pandas, NumPy, and scikit-learn to preprocess data, define label schemas, and perform QA checks. Coordinated labeling workflows in a research environment for time-series forecasting and asset pricing models. • Defined and maintained label schemas for financial data annotation. • Conducted QA validation of annotations to maintain high data integrity. • Used Python-based tools to automate aspects of the labeling process. • Collaborated closely with student teams on dataset preparation for model training.

2025 - Present

Data Science Analyst Intern (Data Labeling/ML)

OtherTextClassification

Performed data annotation and labeling for supervised learning tasks as part of data science analysis. Defined label schemas, performed QA checks, and maintained annotated datasets for predictive modeling on marketing and customer data. Developed NLP scripts for text classification, sentiment analysis, and entity extraction. • Built automated annotation pipelines in Python for customer feedback data. • Executed text classification using NLTK and spaCy. • Maintained and updated labeled datasets for ongoing model improvement. • Supported quality assurance on annotation work from analysts.

Performed data annotation and labeling for supervised learning tasks as part of data science analysis. Defined label schemas, performed QA checks, and maintained annotated datasets for predictive modeling on marketing and customer data. Developed NLP scripts for text classification, sentiment analysis, and entity extraction. • Built automated annotation pipelines in Python for customer feedback data. • Executed text classification using NLTK and spaCy. • Maintained and updated labeled datasets for ongoing model improvement. • Supported quality assurance on annotation work from analysts.

2024 - Present

Financial Data Analyst (AI/ML Data Preparation & Labeling)

OtherTextClassification

Performed annotated labeling of structured financial datasets for ML portfolio prediction models and risk scenario analyses. Labeled anomalies, market signals, and financial risk events for AI model inputs. Supported quantitative and trading algorithm development with high-quality labeled data submissions. • Developed and maintained structured datasets for reliability in model performance. • Prioritized accuracy and consistency in risk-related annotations. • Applied financial expertise to inform labeling schema design. • Contributed to hyperparameter tuning and feature engineering using annotated datasets.

Performed annotated labeling of structured financial datasets for ML portfolio prediction models and risk scenario analyses. Labeled anomalies, market signals, and financial risk events for AI model inputs. Supported quantitative and trading algorithm development with high-quality labeled data submissions. • Developed and maintained structured datasets for reliability in model performance. • Prioritized accuracy and consistency in risk-related annotations. • Applied financial expertise to inform labeling schema design. • Contributed to hyperparameter tuning and feature engineering using annotated datasets.

2023 - 2024

HighPrecision Image Annotation for Computer Vision Model

Data Annotation TechImageSegmentationBounding Box

This project involved labeling a large dataset of street-level images to train a computer vision model for autonomous vehicle navigation. My tasks included precise object detection and segmentation of pedestrians, vehicles, traffic signs, and lane markings using bounding boxes and polygon annotations. The dataset consisted of over 100,000 images, requiring meticulous annotation and validation to ensure accuracy. I followed strict quality control measures, including inter-annotator agreement checks and model-assisted pre-labeling refinements, to enhance dataset consistency and usability.

This project involved labeling a large dataset of street-level images to train a computer vision model for autonomous vehicle navigation. My tasks included precise object detection and segmentation of pedestrians, vehicles, traffic signs, and lane markings using bounding boxes and polygon annotations. The dataset consisted of over 100,000 images, requiring meticulous annotation and validation to ensure accuracy. I followed strict quality control measures, including inter-annotator agreement checks and model-assisted pre-labeling refinements, to enhance dataset consistency and usability.

2020 - 2024

Data Annotation Specialist (Contract)

OtherTextClassification

Handled annotation and labeling of complex multimodal datasets, including text, images, and structured data, to train and fine-tune AI/ML models across domains. Applied bounding box and classification workflows for computer vision, and NER/entity labeling for NLP. Contributed to RLHF tasks for improving LLM alignment and data robustness. • Executed multi-label and hierarchical image classification using annotation tools. • Performed NER, sentiment tagging, and relation extraction for NLP datasets. • Engaged in RLHF evaluations, giving comparative feedback on model outputs. • Reviewed and resolved annotation discrepancies for consistent dataset quality.

Handled annotation and labeling of complex multimodal datasets, including text, images, and structured data, to train and fine-tune AI/ML models across domains. Applied bounding box and classification workflows for computer vision, and NER/entity labeling for NLP. Contributed to RLHF tasks for improving LLM alignment and data robustness. • Executed multi-label and hierarchical image classification using annotation tools. • Performed NER, sentiment tagging, and relation extraction for NLP datasets. • Engaged in RLHF evaluations, giving comparative feedback on model outputs. • Reviewed and resolved annotation discrepancies for consistent dataset quality.

2023 - 2023

Education

Work History