Data Scientist
Tasks
- Apply generative AI with LLM methods
- Build AI enabled data products and analytics prototypes
- Build machine learning models and statistical models
- Build reusable patterns for prompts features evaluation and monitoring
- Communicate findings model outputs limitations and recommendations
- Design optimization and causal experimental analysis
- Develop forecasting classification clustering anomaly detection and recommendation solutions
- Evaluate model accuracy precision recall relevance and explainability
- Implement entity resolution and entity matching
- Move solutions from prototype to production with monitoring and documentation
- Partner with stakeholders to define problems and success criteria
- Work with structured and unstructured enterprise data
Perks/Benefits
- N/A
Skills/Tech-stack
Agentic Workflows | Anomaly Detection | Causal Inference | Classification | ClickHouse | Clustering | Databricks | Embeddings | Entity Resolution | Experiment design | Experimentation | Feature Engineering | Forecasting | Generative AI | Language Models | Large Language Models | Machine Learning | Model Monitoring | Observability | Optimization | Prompt engineering | Python | RAG | Recommendation Systems | Reproducibility | Responsible AI | Retrieval-Augmented Generation | SQL | Semantic Search | Snowflake | Spark | Statistical modeling | Text-to-SQL | Version control
Education
N/A
Roles
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