Adithya R.

Key Skills

Software

Label Studio

Top Subject Matter

Search query understanding

spell check

and type-ahead training data quality

Top Data Types

Text

Image

Document

Top Task Types

Fine-tuning

Classification

Bounding Box

Entity (NER) Classification

Question Answering

Text Generation

Text Summarization

RLHF

Transcription

Prompt + Response Writing (SFT)

Freelancer Overview

Staff Data Scientist, Walmart Global Tech (2019–2021). Brings 12+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Doctor of Philosophy, Arizona State University (2014) and Master of Science, Arizona State University (2014). AI-training focus includes data types such as Text and labeling workflows including Fine-tuning, Evaluation, and Rating.

Labeling Experience

Senior Manager, Data Science, Walmart Global Tech (2021–Present)

Text

Led neural ranking and semantic retrieval improvements for query autocomplete using rigorous offline evaluation and online A/B testing. Designed and operationalized experimental metrics and frameworks (e.g., NDCG, MRR, engagement) to validate the impact of model changes. Prioritized PoC-first validation to ensure utility before scaling investment in applied ML solutions. • Improved autocomplete success rates via offline evaluation and online A/B testing. • Established experimentation frameworks and metrics design (NDCG, MRR, engagement). • Drove PoC-first strategy to prove ML utility before production scaling. • Led applied science and engineering roadmap for discovery platforms.

2021 - Present

Staff Data Scientist, Walmart Global Tech (2019–2021)

TextFine-tuning

Built LLM-guided training data quality improvement workflows for models used in spell check and type-ahead. Evaluated and refined training datasets to improve downstream model performance under real-time constraints. Owned experimentation and iteration loops to raise data reliability before and during model deployment. • Improved training data quality for spell check and type ahead use cases. • Supported latency-constrained production inference pipelines for model usage. • Orchestrated large-scale batch inference to support query and content understanding. • Designed sequence-to-sequence translation models to power real-time spell check.

2019 - 2021

Education

Work History