Simon Aytes

About

I move quickly from rough ideas to working AI systems, then do the engineering needed to make them reliable. I work with stakeholders to turn business needs into technical direction, iterating quickly toward systems that can make it to production and stay there.

Experience

2021 – 2024, 2026 – Present

Penta Group

Senior Data Scientist / Data Scientist

Penta is a global strategy and communications consultancy where my work has moved from client-facing NLP and media-intelligence systems into company-wide AI tooling. Across both stints, the through line has been embedded engineering: understanding analyst workflows, finding the right technical shape for ambiguous needs, and shipping tools that hold up in live client delivery.

• Built NLP and social-listening systems for media intelligence across social and traditional news data.
• Returned to design and ship internal AI tooling used by analysts across the company.
• Reworked notebook-era infrastructure into API-first services, worker execution, persistence, and observability.

Feb 2024 – Dec 2025

KAIST MLAI Lab

Graduate Research Assistant

At KAIST's MLAI Lab, I worked on efficient LLM reasoning and test-time compute. The work sharpened the research side of my AI practice: reading quickly, testing ideas rigorously, and turning model-behavior questions into concrete methods that can be evaluated against real reasoning workloads.

• First-authored Sketch-of-Thought, an EMNLP 2025 paper on efficient LLM reasoning.
• Developed an inference-time method that reduced reasoning tokens by roughly 84% on average.
• Explored training-free methods alongside GRPO and supervised fine-tuning approaches.

Jan 2025 – Present

Lighthouse Analytics

AI Engineering Consultant

Lighthouse Analytics is the name I use for independent AI engineering work. The main engagement was a production invoice-intelligence system for a small-business client, scoped from discovery with non-technical stakeholders through rollout. It used LLM-driven OCR, OpenAI structured outputs, and Pydantic schemas to turn messy vendor documents into dependable business data.

• Processed roughly 3,000 invoices per month across 100+ vendor formats.
• Saved an estimated 2,000 working hours and $30,000+ in annual operating costs.
• Helped the client scale a high-volume back-office workflow without adding headcount.

Aug 2021 – Dec 2021

The Wall Street Journal

Journo-Tech Fellow

At The Wall Street Journal, I worked on applied machine learning for editorial analytics. The fellowship was an early exposure to building against a real product environment: using internal content data, preparing training sets from production databases, and framing model outputs around decisions an editorial team might actually care about.

• Designed and trained a ML model to predict article performance based on its headline.
• Trained a model on 13,000+ metadata-rich articles from WSJ's content databases.
• Embeded with the News Insights team to develop data-driven solutions for Journalists.

2019 – 2021

NASA Langley Research Center

Development Intern

Across several internships at NASA Langley, I built AR and VR tools for researchers working with 3D data. The work combined software development, data visualization, and user research: talking with researchers, understanding how they inspected technical data, and turning those workflows into interactive Unity prototypes.

• Led development of a VR heat-map visualization tool adopted by Langley's Engineering Design Studio.
• Interviewed 13 researchers to shape requirements and interaction design.
• Built Unity3D prototypes with integrated data processing, analysis, and visualization workflows.

May 2022 – Oct 2022

York College, CUNY

Researcher

At York College, I supported public-interest research on indigent burials by helping turn scattered public records into a structured research dataset. The project sat at the edge of data engineering and social research, with an emphasis on feasibility, source coverage, and making messy public information usable for analysis.

• Designed an automated scraping and extraction pipeline for state and local records.
• Helped assess the feasibility of a national database across jurisdictions.
• Contributed analysis supporting a Population Association of America research abstract.

Selected Work

Skills

Language Models

RAG·Agents & tool use·MCP·Prompt & context engineering·Structured outputs

Applied AI

Fine-tuning (SFT, GRPO)·Inference optimization·Anthropic & OpenAI SDKs

ML & Data

PyTorch·NLP·scikit-learn·pandas·NumPy·SQL

Engineering

Python·TypeScript·FastAPI·React·PostgreSQL·REST APIs·Git

Cloud & Infra

AWS (ECS, EC2, S3, SageMaker)·Docker·CI/CD (CodePipeline, CodeBuild)·Linux

Education

Contact

If you think we could work well together, feel free to reach out.