AI-First Product Manager (Ops Transformation)

Trusting Social & Kompato AI • Dallas, TX

107d

About The Position

We’re transforming the debt collection industry with AI-powered automation that delivers compliant, empathetic, and revenue-driving customer experiences. Our mission is to help agencies, lenders, and financial institutions reimagine collections by blending human-level conversation with the efficiency of AI. We’re a fast-growing fintech startup building the operating backbone for modern collections—whether inbound, outbound, or digital self-service. If you’re energized by stripping down messy, manual processes and rebuilding them as AI-native workflows, and if you can translate operational pain into automation that sticks, this role might be for you. We are looking for our first AI-First Product Manager (Ops Transformation) — a builder who fuses product management rigor with systems-level AI thinking. Your mandate: take our debt collection operator’s playbook, strip it to the studs, and redesign it for an AI-native organization. That means immersing yourself in daily ops, identifying compliance-heavy bottlenecks, and rethinking how they should work in an AI-first world. You’ll own the journey from raw operator pain → prototype automation → adoption and scaling. This is a role for someone equally comfortable shadowing frontline staff, drafting RICE + risk-scored automation priorities, and guiding MVP engineering builds (or prototyping in Cursor/Claude code yourself). In short: you’ll turn ambiguity into working automations and repeatable playbooks that compound value across departments.

Requirements

• 5+ years in product management, ops transformation, or program management.
• Proven track record of translating manual SOPs into automated, governed workflows.
• Experience working in or adjacent to regulated industries (fintech, healthcare, collections, gov).
• Familiarity with automation stacks (RPA, workflow orchestration, APIs) and emerging AI architectures (LLMs, agentic systems).
• Data self-service skills (SQL, dashboards) a plus.
• Startup-tested: you can bring order without needing layers of process to get going.

Nice To Haves

• Start with AI: treat automation as the reflexive first step in ops design.
• Human as router: know when to elevate to judgment, compliance, or context.
• Compound wins: document and share so no automation stays siloed.
• Culture builder: normalize AI-first rituals (e.g., 'AI Win of the Week') to drive adoption.
• Execution-first: You thrive in ambiguity, move from pain points to working solutions fast.
• AI-native reflexes: You know LLMs, RAG, embeddings, and agentic orchestration — not just in theory but in how they reshape real workflows.
• Systems thinker: You can zoom out to see the process end-to-end and zoom in to fix the brittle step that breaks it.
• Operator empath: You can shadow a frontline operator and feel where the friction lives.
• Scrappy builder: Bonus if you can prototype in Cursor IDE/Claude code or SQL; at minimum, you’re fluent enough with code to direct engineers effectively.
• Compliance aware: You respect the red lines — automation never compromises legal, regulatory, or reputational guardrails.

Responsibilities

• Immerse in daily collections ops to map SOPs and surface pain points.
• Apply Jobs-to-Be-Done and anthropological methods to reframe operator needs.
• Identify and rank-order automation opportunities by reach, impact, compliance risk, and more (RICE + risk-tier).
• Draft specs and either prototype yourself or manage lightweight engineering builds.
• Ship scrappy MVPs quickly; measure cycle-time, compliance adherence, and throughput.
• Capture learnings in shareable posts, SOPs, and the AI-First Ops Handbook so wins compound.
• Apply strict guardrails: T0–T3 risk tiers, human-in-the-loop where required.
• Define automation governance artifacts (SLAs, SOPs, audit logs).
• Ensure AI workflows are explainable, auditable, and compliant.
• Track adoption: % of ops tasks starting with AI.
• Measure business outcomes: revenue impact, FTE leverage, error reduction.
• Industrialize successful prototypes into repeatable modules.

What This Job Offers

• Number of Employees: 51-100 employees