Data Science Professional
Tasks
- Acquire process integrate and clean data
- Apply data governance data quality and data privacy
- Automate data collection and analytics
- Build data pipelines
- Build data products
- Conduct statistical analysis
- Create dashboards
- Create data visualizations for stakeholders
- Develop advanced analytics using SQL and Python
- Develop machine learning models
- Perform clustering PCA regression and significance testing
- Perform exploratory data analysis
- Run ML pipelines in Kubernetes
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Agentic AI | Apache Spark | Apache Superset | Artificial Intelligence | Cloud platform | Clustering | Component analysis | Data Analysis | Data Cleansing | Data Governance | Data Modeling | Data Privacy | Data Quality | Data Visualization | Data pipeline | Data pipeline automation | Exploratory Data Analysis | Google Cloud | Google Cloud Platform | Hadoop | Kubeflow | Kubernetes | Language Models | Large Language Models | Machine Learning | Pipeline Automation | PostgreSQL | Principal Component Analysis | Python | Qlik | Regression | SQL | Significance Testing | Statistical Analysis | Tableau
Education
N/A
Roles
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