Data Scientist
Tasks
- Augment pipelines with large language models
- Build probabilistic record linkage pipelines
- Conduct production deployment and monitoring
- Create embedding based matching and semantic similarity
- Deliver end-to-end data science projects
- Design solutions and break down epics into user stories
- Develop entity resolution workflows
- Develop reusable libraries and documentation
- Document solution approach metrics and timelines
- Implement DataOps and MLOps best practices
- Monitor models and ensure reproducibility
- Orchestrate pipelines and track experiments
- Perform code reviews
- Scope problems and design approaches
- Select and evaluate machine learning models
Perks/Benefits
Skills/Tech-stack
AWS | Classification | Clustering | Code review | DataOps | Databricks | Delta Lake | Documentation | Embeddings | Entity Resolution | Experiment tracking | Experimental Design | GCP | Language Models | Large Language Models | MLOps | Machine Learning | Model Evaluation | Model Monitoring | Pipeline Orchestration | Probabilistic record linkage | PySpark | Python | Record Linkage | Regression | Reproducibility | Retrieval-Augmented Generation | SQL | Semantic Search | Semantic similarity | Testing | Vector Databases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
Regions
Countries
States
Cities
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