Job Summary We are seeking a Sr Data Scientist to design, build, and operationalize production-grade machine learning solutions that support content production, localization, metadata enrichment, intelligent search, and archival workflows across large-scale media systems. The ideal candidate will have strong expertise in applied machine learning, statistical modeling, MLOps, and cloud-native technologies, with the ability to develop scalable AI solutions and collaborate across engineering, operations, and product teams. Key Responsibilities Design, develop, train, and optimize machine learning models for media metadata extraction, content classification, entity resolution, semantic search, similarity search, and multimodal understanding. Build predictive and prescriptive models to improve content operations, localization quality, asset matching, retrieval ranking, and automated tagging. Perform feature engineering, statistical analysis, model selection, and model optimization using modern machine learning frameworks. Develop scalable machine learning pipelines using Python, cloud-native services, and enterprise data platforms. Collaborate with Data Engineering teams to design data pipelines for model training, validation, and inference. Build evaluation frameworks and monitoring solutions to ensure model quality, reliability, and drift detection. Containerize, deploy, and maintain machine learning services using CI/CD pipelines and orchestration frameworks. Integrate machine learning models with content production systems, APIs, metadata services, and enterprise platforms. Ensure model reproducibility, versioning, and lifecycle management following enterprise MLOps practices. Apply machine learning techniques to content production, localization, content distribution, archival, retrieval, and semantic search use cases. Partner with product managers, engineering teams, operations teams, and business stakeholders to translate business requirements into AI-driven solutions. Communicate technical concepts, model performance, and implementation trade-offs to technical and non-technical audiences. Evaluate emerging machine learning technologies, vector search solutions, embeddings, and cloud-native AI services to drive continuous innovation. Required Qualifications Overall 10+ years of professional experience in Data Science, Machine Learning, Artificial Intelligence, or related technical fields. Strong proficiency in Python, Machine Learning, Statistical Modeling, SQL, and AWS. Experience with Semantic Search, Vector Databases, and Generative AI technologies. Hands-on experience developing, deploying, and operationalizing production-grade machine learning models. Experience implementing scalable MLOps pipelines and machine learning deployment workflows. Strong knowledge of model training, feature engineering, validation, monitoring, and lifecycle management. Experience working with large-scale datasets and distributed computing environments. Strong analytical, problem-solving, and statistical modeling skills. Experience collaborating across engineering, operations, product management, and business teams. Excellent verbal and written communication skills. Preferred Qualifications Experience working with media metadata, content intelligence, localization, or multimodal machine learning. Experience with embeddings, vector search technologies, and knowledge graph solutions. Experience with CI/CD, containerization, and orchestration frameworks. Familiarity with cloud-native AI services and enterprise machine learning platforms. Education: Bachelors Degree
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: compun
- Position Id: GANDC5835225
- Posted 7 days ago
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