Data Scientist

HI

• Title: Sr Data Scientist
• Location: Glendale, CA
• Full time Employment
• Hybrid: Yes
• Interview process: 3 rounds
• One technical video round
• One in-person round with Tavant
• Client Round.

• Skills to be evaluated on
• Python Machine Learning, data science, AWS, Statistical Modeling, Semantic Search, Vector DB, GenAI, SQL

• Role Summary
• The Applied ML Engineer will design, build, and operationalize machine-learning models that power content production, localization, metadata enrichment, archival workflows, and intelligent search/retrieval across large-scale media systems. This role sits at the intersection of applied machine learning, content intelligence, and production-grade engineering—supporting data-driven decisions and automation across the content supply chain.
• Roles & Responsibilities
• 1. Applied Machine Learning & Statistical Modeling
• Develop, train, and optimize models for media metadata extraction, content classification, entity resolution, similarity search, and multimodal understanding.
• Build predictive and prescriptive models to streamline content operations such as localization quality prediction, asset matching, retrieval ranking, and automated tagging.
• Conduct rigorous analysis, feature engineering, and model selection using modern statistical and ML frameworks.
• 2. Production-Grade ML Engineering
• Implement scalable ML pipelines using Python, cloud-native services, and enterprise data platforms.
• Partner with Data Engineering teams to design performant data flows for model training, validation, and inference across high-volume media catalogs.
• Build robust evaluation frameworks and monitoring systems ensuring quality, reliability, and drift detection in production environments.
• 3. MLOps & Model Deployment
• Containerize, deploy, and maintain ML services using CI/CD, orchestration frameworks, and real-time or batch inference architectures.
• Collaborate with platform and infrastructure teams to integrate models with content production systems, search platforms, APIs, and metadata services.
• Ensure reproducibility, versioning, and lifecycle management aligned with enterprise machine-learning practices.