[Remote] Marketing Engine Data Scientist at Zoetis

United States

Not SpecifiedCompensation

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Not SpecifiedVisa

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Advanced degree in Data Science, Computer Science, Engineering, or a related discipline
4+ years of experience in analytics, data science, or decision support
Proven ability to collaborate with business stakeholders to deliver actionable insights
Hands-on experience with campaign analytics, predictive modeling, Gen AI/LLMs, and stakeholder engagement
Expert in Python and PySpark; strong SQL skills; experience with Databricks
Practical experience with Gen AI frameworks, tools, and large language models
Proficient with Git for collaborative code development and management
Experience building and maintaining data pipelines; knowledge of cloud data platforms
Skilled in predictive modeling, segmentation, time series analysis, and Gen AI/LLM applications
Strong ability to communicate complex technical concepts to non-technical audiences

Analyze complex data sets to deliver actionable insights that drive strategic business decisions
Partner with digital marketing and brand managers to define and segment target audiences for campaigns
Design, extract, and refine campaign lists using advanced data science and Gen AI techniques
Analyze campaign performance and outcomes, delivering actionable recommendations to maximize ROI
Serve as a trusted advisor, translating technical findings into clear, actionable business insights
Present complex analyses to both technical and non-technical audiences
Build strong, collaborative relationships across marketing, sales, and other functional teams
Develop and implement predictive models, segmentation, and time series analyses to support marketing strategies
Apply Gen AI frameworks and large language models to drive personalization and campaign effectiveness
Continuously monitor and refine models to ensure accuracy, relevance, and business impact
Utilize Python, PySpark, Databricks, Gen AI frameworks, and SQL for data processing, analysis, and modeling
Collaborate with data engineering teams to ensure robust, scalable data pipelines and infrastructure
Champion best practices in code quality, reproducibility, documentation, and version control (Git)
(Bonus) Apply software engineering skills to streamline analytic workflows and deploy solutions