Job role: 

As one of the first employees, the Junior Data Scientist will work closely with the co-founding team to provide a solid analytical foundation for buyVerde’s mission-critical efforts in data acquisition, enhancement, and awareness. Specifically, the work will involve exploratory data analysis, customer segmentation, evaluation of the quantitative impact of key business decisions, and the development and tuning of models to organize, enhance, and expand buyVerde’s core data asset.

There are 3 main areas where the Junior Data Scientist will be critical to buyVerde’s business growth:

• Work directly with co-founding team to create metrics and a visual dashboard to track buyVerde’s growth versus targets. Develop ongoing strategy to improve KPI metrics and integrate with business plan.
• Responsible for KPI metrics dashboard and monitoring including but not limited to the following categories and business areas:
  • Product catalog
  • Vendor relations
  • User behavior and activity
  • Sales
  • Data quality

• Responsible for the operation, improvement, and interative automation of buyVerde’s proprietary Standards Board Review process, which leverages our core data asset to verify vendor ESG data and prevent greenwashing in company profiles and product listings.
• Use a combination of scripting, SQL, and low-code tools to streamline and improve this product for non-technical business users

• Develop automated capabilities to enrich buyVerde’s data by automatically recognizing and tagging brand and product ESG values
• Work with co-founding team to improve buyVerde’s named entity recognition capabilities with natural language processing, enabling faster and more accurate matching of brands and products to their respective certifications at scale

Qualifications:

• Must be comfortable working in a startup environment and very closely with senior management and executives
• Experience solving quantitative business problems with at least one scripting language (Python preferred, or R) and SQL
• Understanding of machine learning fundamentals, especially for natural language applications