Senior Data Scientist and Solution Architect
Tasks
- Apply supervised and unsupervised learning
- Build RAG pipelines
- Conduct time series analysis
- Design agentic workflows
- Design end-to-end data pipelines
- Develop LLM-powered applications
- Develop predictive analytics models
- Drive MLOps model lifecycle
- Fine-tune LLM models
- Implement prompt engineering guardrails
- Integrate research into production
- Lead AI/ML solution architecture
- Manage embeddings retrieval reranking
- Mentor engineers and enforce technical governance
- Perform model evaluation and statistical validation
- Translate business objectives into data science projects
Perks/Benefits
- N/A
Skills/Tech-stack
Agent Orchestration | Agentic Workflows | Bias Mitigation | Deep learning | Embeddings | Experimental Design | Fine Tuning | Human-in-the-loop | Kubeflow | LLM | MLOps | MLflow | Machine Learning | Model Evaluation | Model Interpretability | Multi-Agent | Multi-agent orchestration | Optimization | Pandas | Predictive Analytics | Prompt engineering | PyTorch | Python | RAG | SQL | Scikit-learn | Semantic Retrieval | Series analysis | Snowflake | Spark | Statistical validation | Supervised Learning | TensorFlow | The Loop | Time Series | Time Series Analysis | Unsupervised Learning | Vector Databases
Education
Roles
Architect | Data Scientist | Scientist | Senior Data Scientist | Solution Architect
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