
Snap Finance
Snap Finance, operating within the financial services industry, is currently hiring. With a company size of 1,001-5,000 employees, Snap Finance is seeking qualified individuals to join its team. Snap Finance currently has three open positions: * **Software Engineer, Machine Learning:** This on-site role involves designing and developing machine learning systems. Key responsibilities include building and scaling advanced ML models, designing and shipping end-to-end ML systems, and mentoring other engineers. This role requires a MS or PhD in a quantitative field or a BS with robust experience. The ideal candidate will have 7+ years of experience in machine learning, artificial intelligence, recommendation systems, or data mining, along with strong software engineering skills and experience with various technologies. * **Care Resolution Specialist:** This on-site position focuses on supporting internal LTO Customer Service agents. The Care Resolution Specialist will provide guidance, handle escalated calls, and identify opportunities for departmental improvements. Successful candidates will possess at least 2 years of experience in a call center or customer support environment. Strong communication, organizational, and problem-solving skills are essential. * **Area Sales Manager:** This on-site role involves working autonomously within a defined territory to drive sales. The Area Sales Manager will be responsible for contacting merchant partners, building relationships, and utilizing data to uncover revenue potential. The position requires 3–4 years of outside B2B selling experience or 7+ years of experience in outside B2B sales. Candidates should demonstrate a proven track record of achieving sales goals and have excellent communication and time management skills. The roles at Snap Finance utilize a variety of technologies. The Software Engineer, Machine Learning position requires experience with Machine Learning, Artificial Intelligence, Data Mining, Python, Java, Deep Learning, Statistical Analysis, SQL, AWS, Data Pipelines, Feature Engineering, Credit Risk Modeling, Distributed Systems, Automated Workflows. All three open positions are on-site roles. Here are some frequently asked questions about working at Snap Finance and applying for these positions: 1. **What is the company size of Snap Finance?** Snap Finance has 1,001-5,000 employees. 2. **What type of experience is needed for the Area Sales Manager role?** The Area Sales Manager role requires 3–4 years of outside B2B selling experience or 7+ years of experience in outside B2B sales. 3. **What are the requirements for the Software Engineer, Machine Learning position?** The Software Engineer, Machine Learning position requires a MS or PhD in a quantitative field or a BS with robust experience. 4. **What are the key responsibilities of the Care Resolution Specialist?** The Care Resolution Specialist is responsible for supporting internal agents, providing guidance, handling escalated calls, and identifying opportunities for improvements. 5. **Are the available positions remote?** No, all the currently listed positions are on-site roles.
About the Company
Snap Finance harnesses the power of data to empower consumers of all credit types to get what they need. Launched in 2012, Snap’s technology utilizes more than a decade of data, machine learning, and nontraditional risk variables to create a proprietary platform that looks at each customer through a more holistic lens. Snap’s lease-to-own and credit solutions are changing the face and pace of consumer retail finance.
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3Company Overview:At Snap Finance, we believe everyone deserves access to the things they need, regardless of credit history. Since 2012, we've used data, machine learning, and a more human approach to...
Company Overview:At Snap Finance, we believe everyone deserves access to the things they need, regardless of credit history. Since 2012, we've used data, machine learning, and a more human approach to...
Company Overview:At Snap Finance, we believe everyone deserves access to the things they need, regardless of credit history. Since 2012, we've used data, machine learning, and a more human approach to...
