Data Scientist

real-world KPIs, SQL & Python Mastery: Strong in frameworks such as Pandas/NumPy/Scikit-learn…building reliable data pipelines, model training and evaluation. MLOps Fundamentals: Experience deploying/maintaining models (batch or real-time), versioning, CI/CD basics, observability, and reproducible training. Communication & Ownership: Clear with technical/non-technical stakeholders; can scope, prioritize, and explain tradeoffs. Comfortable with uncertainty, data quality issues, leakage risks, and market dynamics (location, seasonality, inventory shifts). Nice to Have: Software engineering experience; multimodal/vision experience; voice AI (ASR/NLU) exposure. Academic Background: Bachelor’s in STEM (Master’s a plus).