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.