Senior Data Scientist
Tasks
- Apply RAG and CAG patterns
- Build embeddings and vector search solutions
- Conduct exploratory data analysis
- Conduct hyperparameter tuning and validation
- Deploy and integrate models into production
- Design and evaluate generative AI solutions
- Design develop and validate statistical and machine learning models
- Develop and evaluate machine learning solutions
- Evaluate model performance and error analysis
- Implement MLOps versioning CI CD monitoring and observability
- Mentor junior data scientists and provide technical leadership
- Mitigate hallucinations with guardrails
- Monitor refine and retrain models
- Translate business needs into data science problems
- Use prompting techniques and manage output variability
Perks/Benefits
- N/A
Skills/Tech-stack
ANN | AWS | Apache Airflow | Apache Spark | Azure | CAG | CI/CD | CNN | Classification | Cloud platform | Clustering | Computer Vision | Databricks | Deep learning | Embeddings | Experimentation | Forecasting | GRU | Google Cloud | Google Cloud Platform | Hive | Hypothesis Testing | Kubeflow | LSTM | Language Processing | MLOps | MLP | Machine Learning | Model versioning | Monitoring | Natural Language | Natural Language Processing | NumPy | Observability | Pandas | Presto | Prompt engineering | PyTorch | Python | RAG | RNN | Regression | Reinforcement Learning | Retrieval-Augmented Generation | SQL | Scikit-learn | Semantic Search | Series analysis | Statistical modeling | Supervised Learning | TensorFlow | Time Series | Time Series Analysis | Transformer Models | Unsupervised Learning | Vector Databases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
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