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RecargaPay seeks a Credit Collection Specialist to lead high-complexity, strategically impactful credit collection projects. This role defines standards, frameworks, and best practices for the collections discipline, serves as a technical reference and mentor for the team, collaborates with leadership on roadmap and priorities, and represents the company in external forums and industry communities.
Key Responsibilities:
- Lead complex credit collection projects from diagnosis through implementation, with measurable strategic impact.
- Define standards, frameworks, and best practices for the collections discipline.
- Design and calibrate collection policies and rules (segmentation, channel, timing, offers) by product and portfolio.
- Analyze portfolio performance (roll rate, vintage, cure, recovery) and translate results into actionable recommendations.
- Optimize credit cost by prioritizing initiatives with measurable impact on loss and collection costs.
- Conduct experiments (A/B, champion/challenger) and interpret results rigorously.
- Apply AI and automation (propensity models, segmentation, LLMs, agents) to collections and analytical workflows.
- Propose and implement process improvements with Operations, Product, Engineering, Risk, and Legal teams, ensuring regulatory compliance (LGPD, CDC, Central Bank).
- Serve as technical reference and mentor for the team, elevating analytical capability.
- Collaborate with leadership on roadmap definition and represent the company externally.
The role is fully remote and offers competitive salary, home office stipend via RecargaPay app, health and dental coverage without co-pay, life insurance, flexible meal vouchers, and wellness benefits.
Requirements:
- Solid experience in collection policies, recovery, or credit within fintech, banking, or financial retail.
- Advanced SQL and Python (or R) for autonomous analysis.
- Proven track record of reducing loss or cost, with before/after evidence.
- Hands-on experience with experimentation (A/B, champion/challenger).
- Concrete use of AI in work (ML, LLMs, or agents) with demonstrable examples.
- Differentiators: payment propensity models or collection scoring; digital credit products (cards, loans, BNPL); Databricks or equivalent analytics stack.
Key Competencies:
- Analytical thinking: decides by evidence and articulates hypotheses.
- Business and cost acumen: links each proposal to financial impact.
- Autonomy and ownership: drives problems from diagnosis to implementation.
- Influence without authority and clear communication.
- Mentorship and technical leadership: shares knowledge and elevates team standards.
