About Us
We are an established Independent Sales Organization with seven years of experience in the credit-card-processing and merchant-services industry. We understand how merchants evaluate processors, why they consider switching, the objections they raise and the factors that ultimately drive conversion.
We are building a proprietary, AI-driven lead-intelligence platform to identify and prioritize merchants most likely to become customers. Our goal is to combine our industry knowledge, current sales-call recordings, operational information and external business data into a system that makes prospecting more scientific, targeted and measurable.
We are seeking an entrepreneurial data-science leader to design and lead this initiative from research and prototype development through prospective testing and commercialization.
The Opportunity
This is not a conventional data-science position in which a completed dataset and predefined model are handed to you. You will help determine what data matters, how it should be collected and labeled, and how qualitative sales intelligence can be transformed into quantitative predictions.
Because our historical labeled data is limited, the initial work will include analyzing current sales-call recordings, interviewing members of our sales organization and identifying the characteristics, circumstances and behaviors associated with merchants who are receptive to switching processors.
The successful candidate will build an initial lead-scoring methodology, test it against real-world sales results and continuously improve it as new outcome data is generated.
This is a founding-level opportunity with salary and equity participation in the resulting platform. The right candidate will have substantial autonomy and the opportunity to create a potentially valuable commercial product within an established operating business.
Responsibilities
- Develop the overall research, data and AI strategy for a proprietary merchant lead-intelligence platform.
- Analyze recorded sales conversations using transcription, natural-language processing, large language models and human-reviewed qualitative research.
- Create a structured framework for extracting relevant information from calls, including merchant characteristics, processing concerns, switching triggers, objections, urgency, decision-maker engagement and sales outcomes.
- Interview sales representatives and company leadership to capture domain knowledge that may not appear in existing structured data.
- Design reliable labeling and quality-control procedures for call transcripts and sales outcomes.
- Identify the signals most likely to predict meaningful sales progression, including statement submissions, follow-up meetings, proposals and signed merchant accounts.
- Develop an initial transparent, rul