Data Science Chief Expert, CX
Tasks
- Apply machine learning deep learning statistical modeling and optimization methods
- Build and deploy AI capabilities in production environments
- Build semantic and contextual foundation for AI agents
- Design and maintain enterprise ontologies and semantic models
- Develop and operationalize end to end machine learning and AI solutions
- Develop generative AI and LLM based solutions
- Harmonize enterprise data into unified semantic or analytical layers
- Partner with cross functional teams for scalable production ready solutions
- Translate business challenges into AI use cases and measurable outcomes
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Azure | Business Process | Business Process Intelligence | Cloud platform | Data Governance | Data Preprocessing | Data mesh | Data product | Databricks | Deduplication | Deep learning | Embeddings | Entity Resolution | Experimentation | Feature Engineering | Generative AI | Google Cloud | Google Cloud Platform | HANA Cloud | Identity stitching | Knowledge graphs | Language Models | Large Language Models | Machine Learning | Model Validation | OWL | Ontology Engineering | Process Intelligence | PyTorch | Python | RDF | RDFS | Retrieval-Augmented Generation | SAP Datasphere | SAP HANA | SAP HANA Cloud | SKOS | SPARQL | SPARQL endpoint | SPARQL query | SQL | Scikit-learn | Semantic Retrieval | Shacl | TensorFlow | Vector Databases
Education
Roles
Data Science | Data Science Lead | Data Scientist | Engineer | Lead | Learning Engineer | Machine Learning Engineer | Science Lead | Scientist
Regions
Countries
States
Cities
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