Job Description
Job Description:
Drive strategic business value by leading the end-to-end development and deployment of Machine Learning solutions. The primary focus is to build robust, data-driven decisioning systems that help clients optimize revenue, maximize asset returns, and automate complex lifecycle strategies.
Responsibilities :
End-to-End Modeling: Build and train survival/hazard-based models to estimate sale probabilities and recommend item-level pricing and disposition actions.
Data & Feature Engineering: Evaluate data readiness, engineer complex features (demand signals, seasonality, costs), and design workarounds for data gaps.
MLOps & Deployment: Test models in shadow mode, measure real-world performance against benchmarks, and collaborate with Data Engineering to build scalable MLOps pipelines (feature stores, CI/CD).
Leadership & Strategy: Act as the primary technical point of contact for clients. Translate complex business rules into a modeling roadmap and mentor a small team of data scientists through agile delivery.
Skills:
Python, SQL, PySpark, Machine Learning (Survival/Hazard modeling), MLOps, Snowflake, dbt, Tableau, and Team Leadership.
Pricing/Inventory domain expertise, constrained optimization, and cloud-native ML tooling.
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