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Vetted Logistic Regression Professionals
Pre-screened and vetted.
Mid-level Data Scientist specializing in NLP, predictive analytics, and healthcare/enterprise SaaS
Junior Software Engineer specializing in backend systems and applied machine learning
Mid-level Software Engineer specializing in Python backend systems and APIs
Senior Python Developer / AI/ML Engineer specializing in cloud ML microservices
Mid-level Data Scientist / ML Engineer specializing in risk, fraud, NLP and recommender systems
Mid-level Machine Learning/AI Engineer specializing in GenAI, RAG, and LLM inference
Mid-level QA/Systems Analyst specializing in test tooling, observability, and healthcare data migration
Mid-level Data Scientist / AI/ML Engineer specializing in financial services and GenAI
Mid-level AI/ML Engineer specializing in NLP, LLMs, and fraud/AML analytics
Mid-level Full-Stack Developer specializing in React/Node and cloud dashboards
Mid-level Machine Learning Engineer specializing in healthcare risk prediction and GenAI
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Data Scientist specializing in ML, NLP, and scalable data pipelines
Mid-level Data Scientist specializing in NLP, risk analytics, and MLOps
Mid-level Data Scientist specializing in ML, NLP, and forecasting across finance and retail
Junior Data & AI Analyst specializing in BI, LLM applications, and analytics
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Rodolfo Lopez
Senior Math Educator transitioning to Data Science & Business Analytics
“Recent McCombs School of Business (UT Austin) Post Graduate Program graduate in Data Science & Business Analytics with hands-on project experience spanning stock clustering/segmentation and hotel booking-cancellation prediction. Strong in end-to-end analysis workflows (EDA, cleaning, feature engineering) and rigorous model comparison/selection, with exposure to boosting methods and imbalanced-data techniques; limited experience so far with embeddings/vector databases and production deployment.”