Paystone

Sales Development Rep

• Identify and connect with potential customers that fit our Ideal Customer Profile (ICP).
• Conduct high-volume outreach via phone, email, and social channels, maintaining 80+ dials per day to generate qualified leads.
• Research organizations and decision-makers to personalize outreach and strengthen engagement.
• Qualify inbound and outbound leads to ensure strong alignment before handoff to Account Executives.
• Execute live call transfers to Account Executives when prospects are ready to engage.
• Manage lead progression through the sales funnel using CRM tools, ensuring data integrity and visibility.
• Collaborate with Account Executives to develop territory strategies and drive pipeline growth.
• Share insights, ideas, and best practices with your peers to strengthen team performance.
• Maintain consistent follow-up with all leads to nurture relationships and keep opportunities moving.

Sales Development Representative

• Identify and connect with potential customers that fit our Ideal Customer Profile (ICP)
• Conduct high-volume outreach via phone, email, and social channels, maintaining 80+ dials per day to generate qualified leads
• Research organizations and decision-makers to personalize outreach and strengthen engagement
• Qualify inbound and outbound leads to ensure strong alignment before handoff to Account Executives
• Execute live call transfers to Account Executives when prospects are ready to engage
• Manage lead progression through the sales funnel using CRM tools, ensuring data integrity and visibility
• Collaborate with Account Executives to develop territory strategies and drive pipeline growth
• Share insights, ideas, and best practices with your peers to strengthen team performance
• Maintain consistent follow-up with all leads to nurture relationships and keep opportunities moving

Sales Development Representative, LATAM

Data Scientist

• Partner with Product and BI to turn business goals into well-scoped ML problems with clear success criteria and guardrails.
• Build and productionize models for segmentation, churn, LTV, and recommendations.
• Design evaluation frameworks including offline benchmarks and online feedback loops to ensure models are measured, not assumed.
• Collaborate with Engineering on production design considerations like latency, payload structure, fallbacks, and failure modes.
• Monitor model performance over time, detect drift, and own retraining and iteration cycles.
• Use product telemetry and user feedback to drive continuous model and feature improvements.
• Work alongside AI coding agents to accelerate development (pipelines, tests, refactoring, exploration) while maintaining human oversight.
• Communicate results, trade-offs, and recommendations clearly across technical and non-technical teams.

AI Engineer

• Work directly with teams and customers to identify high-impact opportunities for AI and workflow automation.
• Design, build, and deploy AI agents and intelligent systems into production environments.
• Lead discovery sessions to understand workflows, pain points, and business requirements.
• Translate complex business challenges into practical technical solutions.
• Rapidly prototype ideas and iterate based on real-world feedback.
• Integrate AI solutions with existing systems, applications, and data sources.
• Build reusable frameworks and components that accelerate future AI initiatives.
• Monitor adoption, performance, and effectiveness of deployed solutions.
• Collaborate across technical and non-technical teams to deliver meaningful outcomes.
• Stay current with emerging AI technologies and identify opportunities to apply them.

AI Developer

• Partner with Product and BI to turn business goals into well-scoped ML problems with clear success criteria and guardrails
• Build and productionize models for segmentation, churn, LTV, and recommendations
• Design evaluation frameworks including offline benchmarks and online feedback loops to ensure models are measured, not assumed
• Collaborate with Engineering on production design considerations like latency, payload structure, fallbacks, and failure modes
• Monitor model performance over time, detect drift, and own retraining and iteration cycles
• Use product telemetry and user feedback to drive continuous model and feature improvements
• Work alongside AI coding agents to accelerate development (pipelines, tests, refactoring, exploration) while maintaining human oversight
• Communicate results, trade-offs, and recommendations clearly across technical and non-technical teams