AI Data Analyst - Merchant Acquisition

Paynada•Remote, US,

8d•$70,000 - $80,000•Remote

About The Position

The company is seeking an entrepreneurial data-science leader to design and lead the development of a proprietary AI-driven merchant lead-intelligence platform, from initial research and prototype development through prospective testing and commercialization. The platform will combine the company’s industry knowledge, sales-call recordings, operational information and external business data to identify and prioritize merchants most likely to become customers. This is a founding-level opportunity rather than a conventional data-science role with a completed dataset and predefined model. The successful candidate will determine what data matters, how it should be collected and labeled, and how qualitative sales intelligence can be converted into quantitative predictions. Initial work will include analyzing current sales-call recordings, interviewing sales personnel and identifying the characteristics, circumstances and behaviors associated with merchants receptive to switching processors. The candidate will build an initial lead-scoring methodology, test it against real-world sales results and improve it as new outcome data becomes available. The role offers substantial autonomy, a competitive salary and equity participation in the resulting platform. It is full time, remote and eligible for a full benefits package.

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 or 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 issues such as data leakage, overfitting and selection bias.
Ability to combine qualitative business research with quantitative analysis.
Strong product judgment and 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 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 merchant characteristics, processing concerns, switching triggers, objections, urgency, decision-maker engagement and sales outcomes from calls.
Interview sales representatives and company leadership to capture domain knowledge not represented in existing structured data.
Design reliable labeling and quality-control procedures for call transcripts and sales outcomes.
Identify signals 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 for use while labeled data is limited.
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 for identifying new prospects.
Design controlled sales experiments comparing AI-selected leads with leads selected through the existing process.
Establish 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 existing workflows.
Build or manage development of an initial working prototype and subsequent production platform.
Develop 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.
Audit available information, including sales-call recordings, CRM information and business outcomes.
Develop a taxonomy for merchant needs, dissatisfaction, switching triggers, objections and levels of sales progression.
Build and validate an AI-assisted process for transcribing, reviewing and structuring sales conversations.
Recommend external data sources and determine which observable merchant characteristics can support prospect identification.
Deliver a working prototype and a roadmap for developing a defensible production platform.