AI Data Analyst - Merchant Acquisition

Paynada• US,

10h•$70,000 - $80,000•Remote

About The Position

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. 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.

Requirements

Advanced training or equivalent professional experience in data science, applied statistics, machine learning, computer science, econometrics, quantitative marketing or a closely related field.
Demonstrated experience with predictive modeling, propensity modeling, lead scoring, customer analytics or another classification/ranking problem.
Strong Python and SQL capabilities.
Experience working with unstructured text, call transcripts, conversation analytics or natural-language processing.
Familiarity with large language models and their use in structured information extraction, classification and research workflows.
Ability to work effectively with limited historical data and develop a disciplined data-collection and validation plan.
Understanding of experimental design, prospective validation, model evaluation and common problems such as data leakage, overfitting and selection bias.
Ability to combine qualitative business research with quantitative analysis.
Strong product judgment and an interest in building a commercial platform, not merely producing an academic model or research report.
Ability to work independently, define priorities and communicate directly with company leadership.
High standards for confidentiality, security and responsible handling of recorded conversations and merchant information.

Nice To Haves

Experience in merchant acquiring, credit-card processing, payments, fintech, banking or financial services.
Experience analyzing B2B sales calls or developing sales-intelligence, revenue-operations or customer-acquisition systems.
Familiarity with merchant categories, processing statements, pricing structures, interchange, chargebacks, point-of-sale systems and the reasons merchants change processors.
Experience with speech-to-text systems, embeddings, retrieval systems and LLM evaluation.
Experience combining internal company data with licensed or publicly available external business data.
Experience taking a data product from initial research through deployment and ongoing monitoring.
Previous startup, founding-team or entrepreneurial experience.
Experience managing technical contributors or outside development resources.

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, rules-based scoring model suitable for use while labeled data remains limited.
Progressively develop statistical and machine-learning models as sufficient prospective data becomes available.
Identify and evaluate external data sources that could reveal merchant size, growth, industry, number of locations, technology, ownership changes, hiring activity and other relevant characteristics.
Connect internal conversation insights to externally observable business signals that can be used to identify new prospects.
Design controlled sales experiments comparing AI-selected leads with leads selected through our existing process.
Establish appropriate performance metrics, including conversion lift, precision among top-ranked leads, customer-acquisition cost and expected merchant profitability.
Work with sales leadership to integrate lead recommendations into the company’s existing workflow.
Build or manage the development of an initial working prototype and subsequent production platform.
Develop appropriate documentation, data-governance practices and safeguards for sensitive sales and merchant information.
Help evaluate and recruit additional data, engineering or research talent as the project grows.
Communicate findings and recommendations clearly to business leadership and nontechnical sales personnel.