Principal Specialist, Data Science & Analytics
Tasks
- Apply data mining techniques
- Build predictive machine learning models
- Communicate insights and recommendations to stakeholders
- Create data visualizations and reports
- Define value hypotheses and KPIs
- Design and maintain data pipelines
- Develop and deploy production grade predictive models
- Drive experimentation and continuous model improvement
- Embed models into enterprise systems
- Ensure data security and compliance
- Ensure responsible AI data quality and governance
- Handle model drift and lifecycle governance
- Lead end to end data science delivery
- Own machine learning and AI lifecycle
- Perform statistical analysis
- Promote reproducibility documentation lineage and auditability
- Set production deployment testing monitoring and governance standards
- Track value realization and adoption metrics
- Translate business needs into AI and analytics use cases
Perks/Benefits
- N/A
Skills/Tech-stack
AI Agents | AWS | Artificial Intelligence | Automated retraining | Cloud Computing | Computer Vision | Data Mining | Data Modeling | Data Pipelines | Data Processing | Data Visualization | Databricks | Drift monitoring | Feature Engineering | Generative AI | MLOps | Machine Learning | Microsoft Azure | Model Drift | Model Explainability | Model Monitoring | Model Validation | Model drift monitoring | Model versioning | Optimization | Predictive Modeling | PyTorch | Real Time | Real-time Data | Real-time Data Processing | Responsible AI | SQL | Sensor data | Series forecasting | Statistical Analysis | Streaming Data | TensorFlow | Time Series | Time Series Forecasting
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Regions
Countries
States
Cities
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