Director, Data Science, eCommerce Consumer

Key Skills

Java Programming, Python Programming, Framework, SQL, Big Data Technology, Apache Hadoop, Machine learning techniques

Job Description

This role leads the architectural vision, development, and acceptance across the entire suite of Machine Learning and Artificial Intelligence (MLAI) algorithms and modeling solutions. The Director of Data Science designs and develops never-been-done-before solutions/models that provide 1) predictive intelligence, 2) automation, and 3) self-maintained / low-tech-debt designs.

Role and Responsibilities

• Lead the design and development of all Machine Leaning and Artificial Intelligence (MLAI) algorithms and modeling solutions to ensure it drives explicit financial impact to eCommerce and other divisions
• Own the MLAI Architecture Roadmap to create solutions that both 1) intelligently build upon each other and 2) showcase never-been-done-before solutions, while aligning with business teams’ use cases and releasing in line with business-critical launches
• Lead a joint team of Data Scientists, Data Engineers, and QA Engineers to uniquely develop and oversee all MLAI team deliverables that are deployed into Production.
• Drive automation of highly complex MLAI solutions to minimize tech debt that 1) enables team resources to be used on brand new projects instead of maintaining existing implementations and 2) transfers/democratizes the power of self-service data science to business units
• Oversee and recommend architecture for C360’s dedicated MLAI workspace (i.e. SDX Cluster) in collaboration with the Big Data Platform team

Skills and Qualifications

• Bachelor’s Degree Required; Preferable a PhD or MS in Mathematics, Computer Science, Engineering, or Economics.
• 10+ years’ overall experience with statistical programming expertise. 
• 10+ years’ experience with Big Data mining expertise.
• Experience in supporting consumer products, retail, tech, telecom, or financial industries.
• Experience developing Machine Learning solutions that directly inform and/or enable quantifiable business action, either by providing insights or building analytic applications.
• Experience with technical team management in the Machine Learning space.
• Machine learning expertise in GLM Regression (linear, logit, multinomial), kMeans/Hierarchical Clustering, Principle Component Analysis, & Decision Tree required; Neural Network, Panel Regression, Bayesian Regression, Times Series, & A Priori preferred
• Regular use of data with high-volume (1TB+) & high-dimensionality (500+ variables per schema) required; clear understanding of Hadoop framework with hands-on experience both querying and applying statistics on massive data sets
• Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner

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