Sr Data Scientist

Fulltime Position

Hybrid Position

Interview process:

• 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.

4. Media Domain Expertise (Nice to have)

• Apply ML techniques to domain-specific challenges in:
  • Content production : post-production signals, QC automation, time-coded metadata, and asset lineage.
  • Localization : subtitle/CC alignment, translation quality scoring, automated language metadata enrichment.
  • Distribution formats : asset matching, technical metadata extraction, content packaging intelligence.
  • Archival & retrieval : semantic search, embeddings, similarity models, knowledge graph augmentation.
• Work closely with media pipeline, operations, and creative engineering teams to ensure solutions align to real-world workflows.

5. Cross-Functional Collaboration & Stakeholder Engagement

• Partner with product managers, content operations, engineering teams, and metadata specialists to translate business needs into ML-driven solutions.
• Communicate complex model behavior, trade-offs, and results to technical and non-technical stakeholders.
• Contribute to solution roadmaps and technology evaluations for emerging ML techniques relevant to content intelligence.

6. Continuous Improvement & Innovation

• Stay current on advances in machine learning, multimodal modeling (text/audio/video), vector search, and media AI.
• Drive experimentation around next-generation retrieval models, embeddings, fine-tuning pipelines, and automated metadata generation.
• Evaluate and integrate third-party tools, open-source libraries, and cloud-native AI services to accelerate delivery.

Required Skills & Experience

• Strong proficiency in Python , applied ML, and statistical modeling.
• Practical experience with media metadata, content understanding, search/retrieval, or multimodal ML .
• Hands-on background in MLOps , model deployment, and operationalizing ML workflows.
• Experience working in production-grade environments with large-scale datasets and distributed systems.
• Proven ability to collaborate across engineering, operations, and product teams with clear, concise communication.