Open to opportunities

Maria Eugenia Inzaugarat

@mariaeugeniainzaugar

Senior data scientist building production-grade ML and generative recommendation systems that drive measurable outcomes.

Argentina

Message

What I'm looking for

I’m looking for a team where I can build production ML products—especially generative systems and recommendation pipelines—while partnering with stakeholders. I want ownership of scalable data and model deployment, clear experimentation cycles, and measurable business impact.

I’m a senior data scientist focused on turning complex ideas into production-ready machine learning systems. Recently, I designed and deployed an end-to-end generative predictions system (PhantomWine), using Bayesian Gaussian Models to produce synthetic data profiles for unseen inputs and deliver accurate target characteristics via a production API endpoint.

I build recommendation and ranking logic that balances performance and confidence. I developed a hybrid dislike probability model combining deep learning (PyTorch), collaborative filtering, and content-based methods—trained with negative signals—and iterated through multiple architectures using systematic experimentation.

I also lead the end-to-end pipeline work: from representation learning to scalable data processing. I create embedding-based user preference vectors from high-dimensional spectral and categorical data, generate cohorts using clustering for downstream prediction, and engineer scalable SQL data pipelines for large, unstructured laboratory datasets.

Beyond core modeling, I bring strong cross-functional delivery. I’ve worked with US clients and stakeholders to implement demand forecasting, price elasticity, and market-share loss modeling, and I’ve delivered Google Cloud-based ML solutions; I also prototype advanced generative applications like an LLM healthcare chatbot using RAG and fine-tuning.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Scientist

Current

FirstLeaf

Jan 2024 - Present (2 years 2 months)

Designed and deployed an end-to-end generative predictions system (PhantomWine) using Bayesian Gaussian Models to create synthetic data profiles, predict target characteristics, and estimate user preference probabilities. Delivered a production API endpoint, built representation learning pipelines with embeddings and clustering cohorts, and engineered scalable SQL data processing for laboratory sp

Generative Models PyTorch Collaborative Filtering Content Based Recommendations Representation Learning Data Pipelines Content Based Recommendation

Artificial Intelligence Consultant

Google

Jan 2021 - Jan 2023 (2 years)

Managed and delivered successful implementations of machine learning, AI, and data science solutions with customer technical leads, executives, and partners. Delivered and managed deployment of Google Cloud-based ML solutions, and coordinated project priorities, deliverables, risks, and timelines with stakeholders.

Machine Learning Delivery Google Cloud ML Deployment Stakeholder Management Technical Consulting Project Planning

Data Scientist & Lead

Digital Harvest Inc.

Jan 2019 - Jan 2021 (2 years)

Implemented a sugarcane growth simulation mathematical model based on research using Python and exposed the model via Flask endpoints for backend consumption. Cleaned and analyzed customer geospatial and remote sensing data to build agricultural decision support and machine-learning-based forecasting models, supporting client pilots and proposals.

Python Simulation Remote Sensing Forecasting Agricultural Decision Support predictive modeling

Data Scientist

Fundación Conocimiento Abierto

Jan 2018 - Jan 2019 (1 year)

Analyzed data and developed models for social-impact projects. Performed text mining and sentiment analysis using NLP and built classification models.

NLP Sentiment Analysis Classification Data Analysis

Machine Learning Engineer

Darwoft

Jan 2023 - Present (3 years 2 months)

Worked with a US client to create demand forecasting, optimization algorithms, and price elasticity models for CPG retail. Designed, trained, and deployed end-to-end machine learning models to analyze and forecast market share loss.

Optimization Price Elasticity predictive modeling