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Mid-level ML role in a metro with common ML requirements attracts strong applicant competition.
Core ML and analytics skills transfer across industries, though marketplace/pricing domain experience is advantageous.
Explicit 2–5 years plus required Python, ML libraries, SQL, and productionization skills raise strictness.
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Build and maintain predictive, optimization, and ranking models that influence pricing, shipping, conversion optimization, supplier intelligence, and operational automation.
Design, evaluate, and iterate models based on both technical metrics and business outcomes to ensure reliable production performance.
Collaborate with cross-functional teams including senior data scientists, product, and engineering to deploy production-quality code and scalable solutions.
2-5 years of experience in Data Science, Applied Machine Learning, or Advanced Analytics.
Strong proficiency in Python with ML libraries (scikit-learn, TensorFlow, or PyTorch) and SQL.
Experience with predictive modeling, experimentation, optimization, and working with large datasets; familiarity with distributed computing tools like Spark is a plus.
Ability to write production-quality code and collaborate effectively with engineering teams.
Experienced in delivering rigorous models that balance performance, interpretability, and maintainability in business-critical domains such as pricing and supply chain.
Skilled at experimenting and quickly iterating on models based on experimental feedback and production metrics.
Able to communicate modeling trade-offs and risks clearly to technical stakeholders while contributing reusable code and shared standards.