Data Scientists (SC3)- Data Cleansing & Quality (DQ)
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Key skills for this role
About the Role
We are looking for a Data Scientist to focus on data cleansing and quality tasks using Python Notebooks, SSIS, Fabric Builds, and Power BI. The role involves implementing data cleaning processes, collaborating with cross-functional teams to identify data quality issues, and developing data quality metrics.
Key Skills for This Role
Responsibilities
- Utilize Python Notebooks, SSIS, Fabric Builds, Power BI, and other data engineering tools for data cleansing and quality tasks
- Implement data cleaning processes to ensure accuracy and completeness of datasets
- Collaborate with cross functional teams to identify data quality issues and recommend solutions
- Develop and maintain data quality metrics and reports to track progress
Requirements
- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or related field
- Proven experience with Python Notebooks, SSIS, Fabric Builds, Power BI, and equivalent data engineering tools
- Strong understanding of data quality principles and best practices
- Excellent analytical and problem solving skills
- Ability to work independently and collaboratively in a team environment
Full Job Posting
Key Responsibilities
- Utilize Python Notebooks, SSIS, Fabric Builds, Power BI, and other data engineering tools for data cleansing and quality tasks.
- Implement data cleaning processes to ensure accuracy and completeness of datasets.
- Collaborate with cross functional teams to identify data quality issues and recommend solutions.
- Develop and maintain data quality metrics and reports to track progress.
Qualifications
- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or related field.
- Proven experience with Python Notebooks, SSIS, Fabric Builds, Power BI, and equivalent data engineering tools.
- Strong understanding of data quality principles and best practices.
- Excellent analytical and problem solving skills.
- Ability to work independently and collaboratively in a team environment.
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