Key Responsibilities

POC Design and Delivery Build an end-to-end ML proof of concept in MySQL HeatWave covering data ingestion, feature engineering, model training, evaluation, and inference.
In-Database Modeling Leverage HeatWave ML where appropriate and evaluate tradeoffs between in-database and external model workflows.
Integration and Automation Develop lightweight Python and/or PHP scripts or services to automate data flows, run experiments, and serve inference results.
Feature Engineering and Evaluation Explore datasets, create features, compare model approaches for regression, classification, and forecasting, and produce clear evaluation metrics.
Partner Collaboration Work closely with Oracle, Domo, and internal subject matter experts to absorb best practices and translate them into repeatable implementations.
Documentation and Communication Produce architecture diagrams, assumptions, limitations, evaluation results, and an executive summary that communicates business impact.
Productionization Roadmap Deliver a pragmatic recommendation for next steps including cost estimates, scalability considerations, security and compliance risks, and a prioritized implementation plan.


Required Qualifications:

Experience: 0–2 years of professional experience or equivalent hands-on projects, internships, or academic work.
Machine Learning Fundamentals: Practical or academic exposure to supervised learning and time series forecasting; familiarity with basic model evaluation metrics.
Databases: Strong SQL skills and comfort with relational data concepts.
Programming: Proficiency in Python for data workflows; familiarity with PHP is a plus.
Mindset: Demonstrated curiosity, ability to learn quickly, and willingness to seek mentorship and iterate.
Communication: Ability to document technical work clearly and present findings to both technical and nontechnical stakeholders.
Independence: Able to run experiments, troubleshoot issues, and deliver a demo-ready POC with guidance from senior engineers.