What We Do/Project

We’re looking for a Senior Data Scientist with a strong foundation in predictive modeling, clustering, and statistical analysis. This role combines hands-on machine learning with close collaboration across business and product teams to build production-ready models that forecast content performance and reveal content insights. The ideal candidate is fluent in modern MLOps tools, proactively explores new approaches, and iterates based on user feedback to ensure solutions are interpretable, trusted, and adopted across the organization. Experience in the media/entertainment industry is a strong advantage, or other creative industries. The ideal candidate has experience building DS solutions to support, not replace, human judgment, and therefore has experience optimizing for results beyond just accuracy improvements, such as enriching insights and discussions.

Job Responsibilities / Typical Day in the Role

l Build and iterate on predictive models to project content performance

Test different model types (e.g., gradient boosting, regression) and iterate based on accuracy and user interpretability needs
Complete model experiments to validate hypotheses
Proactively identify opportunities to improve performance and contribute to model development roadmap

Explore and segment content types

Use clustering, principal component analysis, and other unsupervised methods to identify patterns in performance drivers and content types
Experiment with GenAI to enrich data (e.g., augmenting metadata tagging or generating synthetic attributes)

Collaborate with business users to refine models and drive adoption

Infuse models and model approach with users’ domain expertise and decision-making workflows
Present early model outputs in accessible ways to solicit feedback and identify gaps, overlooked variables, and implicit assumptions from users
Interpret user feedback and adapt model design and underlying data to prioritize interpretability, usability, or explainability where required to build trust and drive adoption

Maintain and monitor models

Work with ML Engineers to deploy models in production, including API endpoints, and establish ML pipeline
Contribute to shared libraries and modeling best practices across the data science team

Partner with product and platform teams to deliver impact

Contribute to design and implementation of new products where data science models will be embedded, built by agile product PODs
Collaborate with data architects, data engineers, and broader platform team to facilitate technical discussions and enrich the data available for data science

Must Have Skills / Requirements

Proven experience as a Data Scientist
4+ years of experience
Python and common ML library experience
4+ years of experience; (e.g., scikit-learn, XGBoost, pandas).
Familiarity with AWS tools, especially SageMaker, or equivalent cloud-based ML environments
4+ years of experience

Nice To Have Skills / Preferred Requirements

Experience in media & entertainment industry is a strong advantage but not required
SQL fluency is a plus

Soft Skills

Curiosity and capability to work in an experimental stage of development to test hypotheses and adjust approaches to deliver the most value to business users
Experience building predictive models, especially with limited sample sizes
Understanding of clustering and dimensionality reduction techniques
Exposure to generative AI models (e.g., LLMs, diffusion models) and an interest in applying them to real-world data problems
Strong communication skills and the ability to translate data science work into business value as well as translate business user needs into data science

Technology Requirements

Strong skills in Python and common ML libraries (e.g., scikit-learn, XGBoost, pandas).
Familiarity with AWS tools, especially SageMaker, or equivalent cloud-based ML environments
Experience building predictive models, especially with limited sample sizes
Understanding of clustering and dimensionality reduction techniques
Exposure to generative AI models (e.g., LLMs, diffusion models) and an interest in applying them to real-world data problems

Additional Notes

Sourcing in CA – Burbank.
Hybrid – schedule flexible.

#DICE

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