Data Scientist - SQL/Python

Responsibilities:

Data Exploration and Insights:

• Continuously explore and analyze large datasets from various sources (Excel, databases) to identify opportunities for enhancing data matching logic, including fuzzy logic
• Generate actionable insights to improve data quality within the SCI solution

Data Quality Improvement:

• Perform targeted analyses to identify and address data quality issues
• Propose and implement effective solutions to improve SCI system data integrity

Weekly Playback and Collaboration:

• Participate in weekly playback sessions using Jupyter Notebook to demonstrate insights
• Incorporate feedback from working groups to refine ongoing analysis

Project Scaling and Support:

• Support the scaling of the SCI project by contributing to data acquisition, cleansing, and validation processes for new markets
• Execute pre-batch and post-batch ingestion analysis and validation

Data Analysis and Validation:

• Conduct in-depth validation of SCI records after batch ingestion
• Identify quality gaps and implement improvements proactively

Stakeholder Collaboration:

• Work closely with business stakeholders to validate records requiring manual intervention
• Communicate analytical findings and recommended actions clearly and effectively

Technical Requirements:

• 5+ years of experience as a Data Scientist
• Strong proficiency in

  Python

  and

  SQL

• Hands-on experience using

  Jupyter Notebook

  for analysis and visualization
• Solid understanding of

  data matching techniques

  , including

  fuzzy logic

• Experience with

  large datasets

  from multiple data sources (Excel, databases)
• In-depth knowledge of

  data quality principles

  and

  methodologies

Skills:

SQL, Machine Learning, Data Analysis, Jupyter Notebook, Data Cleansing, Fuzzy Logic, Python, Data Quality Improvement, Data Validation, Data Acquisition, Communication and Collaboration, Problem-solving, Analytical skills

Preferred Qualifications (Optional):

• Experience with dedicated data quality tools and frameworks
• Familiarity with cloud platforms such as

  AWS

  ,

  Azure

  , or

  GCP

• Experience with

  data visualization tools

  (e.g., Tableau, Power BI)
• Knowledge of

  statistical modeling

  and

  machine learning algorithms