Sony Pictures Entertainment (SPE) is looking to solve media and entertainment business challenges by applying advanced analytics, machine learning, and experimentation to drive smarter decisions across production and content distribution businesses.
Requirements
- Strong proficiency in Python and SQL.
- Hands-on experience with generative AI (LLMs, prompt engineering, fine-tuning) and familiarity with open-source frameworks (Hugging Face, LangChain, etc.).
- Experience with statistical modeling and building predictive models.
- Experience with MLOps practices - model lifecycle management, containerization, workflow orchestration (Airflow, Kubeflow).
- In depth knowledge of machine learning techniques, variable reduction, feature importance, and model interpretability
- Familiarity with data visualization tools (Tableau, Power BI, Dash, or Shiny).
- Experience applying GenAI for audience research, recommendation systems, or content insights.
Responsibilities
- Develop and deploy AI/ML models, including generative AI approaches, to solve media and entertainment business challenges.
- Apply LLMs, prompt engineering, and fine-tuning to tasks such as text analysis, summarization, metadata enrichment, and content tagging
- Research, prototype, and test agentic AI frameworks that automate workflows and accelerate data-driven decision making.
- Build and deploy predictive models and advanced analytics that guide business and creative strategies.
- Explore and analyze large, complex datasets to uncover audience insights and optimize content adoption.
- Manage cleansing and feature extraction of large unstructured data and develop insights on top of it
- Partner with engineers to productionalize machine learning models and monitor performance.
Other
- Bachelor's degree in a quantitative field (Statistics, Computer Science, Engineering, Math, etc.); advanced degree a plus.
- 6+ years of applied experience in data science, analytics, or machine learning.
- Excellent communication skills - ability to turn complex data into actionable recommendations.
- Highly analytical, results oriented, have superior organizational skills with strong attention to details and deadlines in a fast-moving environment
- Collaborate with cross-functional partners (marketing, distribution, strategy, finance) to translate data insights into real-world business actions.
