Data Scientist - SQL/Python

Key Responsibilities:

1. Data Exploration and Insights

• Continuously explore and analyze datasets to identify opportunities to improve data matching logic, including fuzzy logic implementations.
• Work with data from diverse sources such as Excel files and relational databases.

2. Data Quality Improvement

• Analyze and identify data quality issues within the SCI system.
• Propose and implement effective solutions to enhance the integrity, consistency, and completeness of data.

3. Weekly Playback and Collaboration

• Participate in weekly playback sessions with stakeholders.
• Use Jupyter Notebook to present insights and incorporate feedback into ongoing analyses and solutions.

4. Project Scaling and Support

• Support the expansion of the SCI project by assisting with data acquisition, cleansing, and validation across new markets.
• Manage batch ingestion processes and validate SCI records post-ingestion.

5. Data Analysis and Validation

• Perform detailed analysis of SCI records to identify inconsistencies or issues after data ingestion.
• Implement improvements to enhance data processing and record matching accuracy.

6. Stakeholder Collaboration

• Work closely with business stakeholders for manual validation of flagged records.
• Clearly communicate data insights, findings, and improvement recommendations.

Technical Requirements:

• 5+ years of experience as a Data Scientist or in a similar analytical role.
• Strong proficiency in Python and SQL.
• Advanced experience using Jupyter Notebook for data exploration and reporting.
• Hands-on experience with fuzzy logic or similar data-matching techniques.
• Proven experience handling large datasets from varied sources.
• Deep understanding of data quality principles, processes, and standards.

Key Skills:

• Python
• SQL
• Jupyter Notebook
• Fuzzy Logic and Data Matching Techniques
• Data Analysis & Cleansing
• Data Validation & Quality Improvement
• Data Acquisition & Preprocessing
• Communication and Stakeholder Collaboration
• Analytical Thinking and Problem Solving
• Machine Learning (Preferred for advanced insights and modeling)

Preferred Qualifications:

• Familiarity with data quality tools and frameworks.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Exposure to data visualization tools like Tableau or Power BI.
• Understanding of machine learning algorithms and statistical modeling.