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GoodLeap

Senior Data Scientist

🌎

Remote, US

3w ago
👀 6 views
📥 0 clicked apply

Job Description

Remote $137,000 - $160,000
About GoodLeap:
GoodLeap is a technology company delivering best-in-class financing and software products for sustainable solutions, from solar panels and batteries to energy-efficient HVAC, heat pumps, roofing, windows, and more. Over 1 million homeowners have benefited from our simple, fast, and frictionless technology that makes the adoption of these products more affordable, accessible, and easier to understand. Thousands of professionals deploying home efficiency and solar solutions rely on GoodLeap’s proprietary, AI-powered applications and developer tools to drive more transparent customer communication, deeper business intelligence, and streamlined payment and operations. Our platform has led to more than $27 billion in financing for sustainable solutions since 2018.
 
GoodLeap is also proud to support our award-winning nonprofit, GivePower, which is building and deploying life-saving water and clean electricity systems, changing the lives of more than 1.6 million people across Africa, Asia, and South America.

Position Summary
We are seeking a Senior Data Scientist with experience building and deploying lead scoring and recommendations models to increase the efficiency of our Consumer Solutions business unit as they sell money-saving and emissions-reducing loans and home improvement projects to our existing customer base.  Our ideal candidate has deep technical skills, is comfortable owning and prioritizing a roadmap independently, and has experience working with business stakeholders to gather requirements, share results, and drive adoption of data models.  In this role, you will scope, prioritize, and execute data modeling projects that will help cross-sell teams prioritize their time and convert leads more effectively.  You will also have the opportunity to work closely with other members of the Analytics team to increase our understanding of the Consumer cross-sell process and guide development of ML-friendly datasets. 

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