What are the responsibilities and job description for the Data Scientist position at Prudent Technologies and Consulting, Inc.?
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.