Seeking a hybrid Data Engineer / Scientist to bridge model development with database processing. You will analyze system data, train predictive models, and build feature stores to support production deployments.
Key Responsibilities
Clean, aggregate, and analyze high-volume business data using Pandas and SQL.
Build, evaluate, and tune machine learning models (classification, regression, clustering).
Write optimized SQL and Python queries to construct database feature stores.
Deploy trained models as scalable inference API endpoints inside Docker containers.
Formulate business hypotheses, run statistical tests, and present insights to stakeholders.
Required Qualifications
4+ years of qualified experience across data analysis, machine learning, and pipeline builds.
Proficient in Python, including numerical and ML libraries (Pandas, Numpy, Scikit-learn).
Strong SQL proficiency, with experience querying databases and processing transactional logs.
Solid mathematical foundations (probability, regression, statistics, linear algebra).
Experience containerizing models and deploying them to cloud staging environments.