Daniel R.

Key Skills

Software

Other

Top Subject Matter

Physics Domain Expertise

Mathematics Domain Expertise

computational modeling (STEM)

Top Data Types

Text

Document

Top Task Types

Classification

Freelancer Overview

Principal Applied Physicist & Computational Modeling Lead, Quantum Physics & STEM Domain (Data Quality Lead). Brings 27+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Professional Education Certificate, Massachusetts Institute of Technology (MIT) (2021) and Master of Science, Brown University (2003). AI-training focus includes data types such as Text and Document and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

Principal Applied Physicist & Computational Modeling Lead, Quantum Physics & STEM Domain (Data Quality Lead)

Text

Evaluated and labeled complex physics, mathematics, and computational model outputs for accuracy, logical consistency, and domain correctness. Developed quality and reliability rubrics to create annotation-ready standards for large analytical datasets. Applied Monte Carlo simulation and statistical inference results to validate model predictions suitable for AI training and review. • Built structured frameworks and rubrics for scientific data quality. • Performed model output evaluation and quantitative error detection. • Collaborated on ground-truth label definitions and label calibration. • Identified edge cases, ambiguous outputs, and classification boundary conditions.

2015 - Present

Senior Physics & Mathematical Modeling Consultant, DataBridge Technologies

OtherTextClassification

Annotated and classified scientific outputs across healthcare, renewable energy, and industrial domains with emphasis on accurate categorization of technical content. Developed labeling taxonomies and classification schemas for physics-based datasets across applied domains. Assessed predictive model outputs against ground-truth physical observations to support RLHF-style preference and ranking needs, while curating and cleaning large datasets to reduce noise. • Created labeling taxonomies and classification schemas. • Performed accuracy checks of predictive outputs versus ground truth. • Applied quality controls to curate and clean large scientific datasets. • Ensured annotation consistency through validation of junior work.

2007 - 2014

Physics & Statistical Modeling Lead, National Institute for Standards and Technology (NIST)

Document

Led quality assurance and measurement validation for scientific data at a national standards body to improve label quality for downstream AI workflows. Developed probabilistic and Bayesian evaluation frameworks to flag inconsistencies in scientific measurements and identify potential mislabeled data. Established reproducible annotation and classification procedures for quantitative research outputs and reviewed technical papers for reasoning quality. • Produced evaluation methods to detect inconsistencies in measurements. • Defined systematic annotation/classification procedures for research outputs. • Scored papers for accuracy, completeness, and reasoning quality. • Partnered with institutions to define ground-truth standards for technical datasets.

2003 - 2007

Education

Work History