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Mid-level data scientist title, 4-6 years, and metro location drive high applicant competition.
Pricing optimization and Bayesian modeling are specialized yet ML skills remain moderately transferable across industries.
Explicit 4-6 years, mandatory pricing/Bayesian expertise and SageMaker experience increases selection strictness.
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Develop and deploy pricing optimization models using statistical, machine learning, and Bayesian techniques to drive client business outcomes.
Analyze pricing elasticity to predict consumer responses and maximize revenue and market share.
Implement machine learning models on AWS SageMaker for scalable, cloud-based performance and collaborate with cross-functional teams to integrate insights into business processes.
4+ years of hands-on experience in data science focusing on pricing optimization and elasticity modeling.
Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Economics, or a related field; advanced degrees preferred.
Expertise in Bayesian modeling and machine learning techniques with proven AWS SageMaker experience for model development and deployment.
Strong programming skills in Python or R and proficiency with statistical analysis libraries (e.g., NumPy, Pandas, PyMC3).
Experienced in pricing optimization analytics with specialization in Bayesian modeling and cloud-based ML deployment on AWS SageMaker.
Able to translate complex statistical modeling outcomes into actionable business insights for diverse clients and cross-functional teams.
Comfortable working on multiple projects in fast-paced environments with a focus on cutting-edge technology and continuous modeling improvements.