Data Scientist

Paramount•New York, NY

About The Position

We are seeking a Data Scientist who is excited to build ML-powered analyses and products that shape business strategy, optimize content, inform marketing investment, and enhance the user experience. You will leverage rich datasets such as video and ad consumption, clickstream activity, subscription history, and 2nd/3rd-party data to build user- and session-level causal and predictive models.

Requirements

2+ years’ experience in Data Science and ML Engineering.
MS or PhD in Statistics, Data Science, Computer Science, or related discipline; or equivalent practical industry experience.
A solid foundation in Python and SQL with comfort writing production-quality code in a collaborative environment.
Ability to autonomously solve standard business problems.
Clear communication with technical and non-technical stakeholders through concise storytelling and visualizations that translate findings into actionable insights.
Judgment to balance technical rigor with functional usability and business adoption.
A willingness to continuously learn new tools and techniques.
Experience with supervised and unsupervised learning methodologies.
End-to-end experience across data exploration, transformation, analysis, and model development, with exposure to productionizing and monitoring.
Strong data fluency: selecting the right inputs, engineering business-relevant features, and validating that findings are reliable.
Familiarity with core statistical/ML methods and model validation fundamentals.
Ability to produce clear technical documentation and stakeholder presentations.
Strong attention to detail with a penchant for data accuracy.

Nice To Haves

Experience using Google Cloud Platform (BigQuery, ML Engine, and APIs).
Experience with integrating AI solutions into existing business processes.

Responsibilities

Translate complex business questions into clear problem statements, success metrics, and actionable quantitative solutions.
Use an iterative approach: start with quick, decision-useful analysis, then refine based on feedback and observed impact.
Implement and maintain reliable ML/analytics pipelines, partnering with engineering as needed to productionize and monitor.
Deliver clear, impactful insights to stakeholders.
Collaborate across the Product organization to operationalize data science solutions that inform strategy and optimize the user experience.
Contribute to data science and product analytics best practices through documentation, code reviews, and knowledge sharing.