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Hui Jin

Hui Jin

Data Scientist - Energy & Industrial Analytics

USA flag
Dallas, Usa
$45.00/hrEntry LevelOther

Key Skills

Software

Other

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor
DocumentDocument

Top Label Types

Classification

Freelancer Overview

I’m a data scientist who builds reliable AI systems by starting with the data: defining clear “what good looks like” criteria, curating/cleaning datasets, and running tight quality checks and error analysis to improve model outcomes. In my Halliburton practicum, I helped deliver a production ML app for casing-collar deformation detection on Azure Databricks, creating reproducible batch pipelines and validation workflows with SMEs to ensure consistent, field-usable results. Across roles in energy and operations analytics, I routinely turn messy, real-world signals (usage + weather, operational logs, service/finance data) into structured training/evaluation datasets, measurable labels/targets, and feedback loops that improve accuracy over time. I’m strong in Python/SQL, data QA, documentation, and cross-functional collaboration—bridging technical teams and stakeholders to ship dependable training data and AI-ready insights.

Entry LevelEnglishChinese Mandarin

Labeling Experience

Casing-Collar Deformation Detection (Halliburton / Azure Databricks)

Other3D SensorClassification
Labeled and curated depth-based wireline well-log data to train/evaluate a casing-collar deformation detection model. Defined a labeling rubric with SMEs (normal vs deformation zones, severity and edge cases), built QA checks and a small gold set, and validated cross-pass consistency scoring on a known-deformation well. Produced reproducible Databricks batch-run summaries for iteration and field handoff.

Labeled and curated depth-based wireline well-log data to train/evaluate a casing-collar deformation detection model. Defined a labeling rubric with SMEs (normal vs deformation zones, severity and edge cases), built QA checks and a small gold set, and validated cross-pass consistency scoring on a known-deformation well. Produced reproducible Databricks batch-run summaries for iteration and field handoff.

2025 - 2025

Anomaly / Pattern Modeling (Utility Fraud + Field Services Analytics)

OtherDocumentClassification
Created a labeled dataset of suspicious vs. normal utility billing/usage records by defining anomaly criteria and exception rules, reviewing edge cases, and performing QA spot-checks to ensure consistency. Cleaned and structured data for model training/evaluation, documented labeling guidelines, and produced outputs that improved anomaly detection and investigation prioritization.

Created a labeled dataset of suspicious vs. normal utility billing/usage records by defining anomaly criteria and exception rules, reviewing edge cases, and performing QA spot-checks to ensure consistency. Cleaned and structured data for model training/evaluation, documented labeling guidelines, and produced outputs that improved anomaly detection and investigation prioritization.

2023 - 2023

QA + Evaluation/Rating + RLHF-style judging

OtherTextQuestion AnsweringText Generation
Applied geoscience domain expertise to evaluate and document subsurface interpretations (GOM/Permian), assessing petroleum-system risk and fluid phase predictions—well aligned with earth-science text evaluation/rating and RLHF-style feedback.

Applied geoscience domain expertise to evaluate and document subsurface interpretations (GOM/Permian), assessing petroleum-system risk and fluid phase predictions—well aligned with earth-science text evaluation/rating and RLHF-style feedback.

2014 - 2021

Education

G

Georgia Institute of Technology

Master of Science, Data Science

Master of Science
2021 - 2025
C

Colorado School of Mines

Doctor of Philosophy, Geology

Doctor of Philosophy
2010 - 2014

Work History

A

Atmos Energy

Data Scientist

Dallas
2025 - Present
B

B. Braun Medical

Data Analyst

Allentown
2023 - 2025