Data Science Manager
Confirmed live in the last 24 hours
Parker

51-200 employees

E-commerce financial management and expense tracking platform
Company Overview
Parker is a financial technology company that offers a comprehensive solution for managing e-commerce finances, providing a unique card for expenses and tracking key metrics to optimize brand potential. Their transparent and responsive approach, as evidenced by their open discussions about credit limits and prompt customer service, fosters trust and ease for accounting teams. With products like the 60 Day Rolling Net Term Cards, Parker demonstrates an understanding of direct-to-consumer business nuances, supporting growth and contributing significantly to the financial tooling stack of e-commerce businesses.
Financial Services
B2B

Company Stage

Seed

Total Funding

$756.1M

Founded

2020

Headquarters

New York, New York

Growth & Insights
Headcount

6 month growth

33%

1 year growth

172%

2 year growth

518%
Locations
New York, NY, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Python
Data Science
R
Data Structures & Algorithms
SQL
CategoriesNew
Data & Analytics
Requirements
  • Bachelor's degree in quantitative field (statistics, math, economics, physics, etc.)
  • 5+ years of experience in roles related to data science, statistics, or machine learning along with 2+ years of experience as a data science manager
  • At least 3 years Experience with a financial institution
  • Experience with commercial and emerging databases, technologies, and languages
  • Experience with applying statistics and machine learning methods to solve business problems
  • Strong knowledge of R , SQL and Python
  • Ability to self-start and self-directed work in an unstructured environment, comfortable dealing with ambiguity and approaching new problems
  • Excellent written and verbal communication to effectively understand business problems and communicate analysis
Responsibilities
  • Develop insights and data visualizations to solve complex problems and communicate ideas to internal stakeholders
  • Partner with data Eng team to iterate on the most effective data structures for the organization
  • Build predictive ML models from development through testing and validation for customer acquisition, underwriting and customer management
  • Extract and analyze data, investigate data integrity, generate metrics and perform ad hoc analysis
  • Partner with data engineers to validate & deploy solutions in an efficient, sustainable & usable manner
  • Research new and enhanced model features to improve our risk decisions
  • Framing and project management of key data science projects with cross functional teams
  • Hire and mentor junior members of the data science team